Methods and systems for predicting response to anti-tnf therapies

ABSTRACT

Methods and systems for administering anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.16/394,046, filed Apr. 25, 2019, which is a continuation ofInternational Application No. PCT/US2019/022588, filed Mar. 15, 2019,which claims the benefit of U.S. Provisional Application No. 62/644,070,filed Mar. 16, 2018, each of which is incorporated by reference hereinin its entirety.

BACKGROUND

Tumor necrosis factor (TNF) is a cell signaling protein related toregulation of immune cells and apoptosis, and is implicated in a varietyof immune and autoimmune-mediated disorders. In particular, TNF is knownto promote inflammatory response, which causes many problems associatedwith autoimmune disorders, such as rheumatoid arthritis, psoriaticarthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis,inflammatory bowel disease, chronic psoriasis, hidradenitis suppurativa,asthma, and juvenile idiopathic arthritis.

TNF-mediated disorders are currently treated by inhibition of TNF, andin particular by administration of an anti-TNF agent (i.e., by anti-TNFtherapy). Examples of anti-TNF agents approved in the United Statesinclude monoclonal antibodies that target TNF, such as adalimumab(Humira®), certolizumab pegol (Cimiza®), golimumab (Simponi® and SimponiAria®), and infliximab (Remicade®), decoy circulating receptor fusionproteins such as etanercept (Enbrel®), and biosimilars, such asadalimumab ABP 501 (AMGEVITA™), and etanercept biosimilars GP2015(Erelzi).

SUMMARY

A significant known problem with anti-TNF therapies is that responserates are inconsistent. Indeed, recent international conferencesdesigned to bring together leading scientists and clinicians in thefields of immunology and rheumatology to identify unmet needs in thesefields almost universally identify uncertainty in response rates as anongoing challenge. For example, the 19^(th) annual InternationalTargeted Therapies meeting, which held break-out sessions relating tochallenges in treatment of a variety of diseases, including rheumatoidarthritis, psoriatic arthritis, axial spondyloarthritis, systemic lupuserythematous, and connective tissue diseases (e.g. Sjogren's syndrome,Systemic sclerosis, vasculitis including Bechet's and IgG4 relateddisease), identified certain issues common to all of these diseases,specifically, “the need for better understanding the heterogeneitywithin each disease . . . so that predictive tools for therapeuticresponses can be developed. See Winthrop, et al., “The unmet need inrheumatology: Reports from the targeted therapies meeting 2017,” Clin.Immunol. pii: S1521-6616(17)30543-0, Aug. 12, 2017. Similarly, extensiveliterature relating to treatment of Crohn's Disease with anti-TNFtherapy consistently bemoans erratic response rates and inability topredict which patients will benefit. See, e.g., M. T. Abreu, “Anti-TNFFailures in Crohn's Disease,” Gastroenterol Hepatol (N.Y.), 7(1):37-39(January 2011); see also Ding et al., “Systematic review: predicting andoptimising response to anti-TNF therapy in Crohn's disease—algorithm forpractical management,” Aliment Pharmacol. Ther., 43(1):30-51 (January2016) (reporting that “[p]rimary nonresponse to anti-TNF treatmentaffects 13-40% of patients.”).

Thus, a significant number of patients to whom anti-TNF therapy iscurrently being administered do not benefit from the treatment, andcould even be harmed. Known risks of serious infection and malignancyassociated with anti-TNF therapy are so significant that productapprovals typically require so-called “black box warnings” be includedon the label. Other potential side effects of such therapy include, forexample, congestive heart failure, demyelinating disease, and othersystemic side effects. Furthermore, given that several weeks to monthsof treatment are required before a patient is identified as notresponding to anti-TNF therapy (i.e., is a non-responder to anti-TNFtherapy), proper treatment of such patients can be significantly delayedas a result of the current inability to identify responder vsnon-responder subjects. See, e.g., Roda et al., “Loss of Response toAnti-TNFs: Definition, Epidemiology, and Management,” Clin. Tranl.Gastroenterol., 7(1):e135 (January 2016) (citing Hanauer et al., “ACCENTI Study group. Maintenance Infliximab for Crohn's disease: the ACCENT Irandomized trial,” Lancet 59:1541-1549 (2002); Sands et al., “Infliximabmaintenance therapy for fistulizing Crohn's disease,” N. Engl. J. Med.350:876-885 (2004)).

Taken together, particularly given that these anti-TNF therapies can bequite expensive (typically costing upwards of $40,000-60,000 per patientper year), these challenges make clear that technologies capable ofdefining, identifying, and/or characterizing responder vs. non-responderpatient populations would represent a significant technological advance,and would provide significant value to patients and to the healthcareindustry more broadly, including to doctors, regulatory agencies, anddrug developers. The present disclosure provides such technologies.

Provided technologies, among other things, permit care providers todistinguish subjects likely to benefit from anti-TNF therapy from thosewho are not, reduce risks to patients, increase timing and quality ofcare for non-responder patient populations, increase efficiency of drugdevelopment, and avoid costs associated with administering ineffectivetherapy to non-responder patients or with treating side effects suchpatients experience upon receiving anti-TNF therapy.

Provided technologies embody and/or arise from, among other things,certain insights that include, for example, identification of the sourceof a problem with certain conventional approaches to defining respondervs. non-responder populations and/or that represent particularly usefulstrategies for defining classifiers that distinguish between suchpopulations. For example, as described herein, the present disclosureidentifies that one source of a problem with many conventionalstrategies for defining responder vs. non-responder populations throughconsideration of gene expression differences in the populations is thatthey typically prioritize or otherwise focus on highest fold changes;the present disclosure teaches that such an approach misses subtle butmeaningful differences relevant to disease biology. Moreover, thepresent disclosure offers an insight that mapping of genes with alteredexpression levels onto a human interactome map (in particular onto ahuman interactome map that represents experimentally supported physicalinteractions between cellular components which, in some embodiments,explicitly excludes any theoretical, calculated, or other interactionthat has been proposed but not experimentally validated), can provide auseful and effective classifier for defining responders vs.non-responders to anti-TNF therapy. In some embodiments, genes includedin such a classifier represent a connected module on the humaninteractome.

Accordingly, in some embodiments, the present disclosure provides amethod of treating subjects with anti-TNF therapy, the method comprisinga step of: administering the anti-TNF therapy to subjects who have beendetermined to display a gene expression response signature establishedto distinguish between responsive and non-responsive prior subjects whohave received the anti-TNF therapy.

In some embodiments, the present disclosure provides, in a method ofadministering anti-TNF therapy, the improvement that comprisesadministering the therapy selectively to subjects who have beendetermined to display a gene expression response signature establishedto distinguish between responsive and non-responsive prior subjects whohave received the anti-TNF therapy.

In some embodiments, the present disclosure provides a kit comprising agene expression response signature established to distinguish betweenresponsive and non-responsive prior subjects who have received anti-TNFtherapy.

In some embodiments, the present disclosure provides a method ofdetermining a gene expression response signature, the method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness of anti-TNF therapy toa human interactome map; and selecting a plurality of genes determinedto cluster with one another in a human interactome map, therebyestablishing the gene expression response signature.

In some embodiments, the present disclosure provides a method ofdetermining a gene expression response signature, the method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness of anti-TNF therapy toa human interactome map; and selecting a plurality of genes associatedwith response to anti-TNF therapy and characterized by their topologicalproperties when mapped on a human interactome map (e.g., genes that areproximal or otherwise close together in space on a human interactomemap), thereby establishing the gene expression response signature.

In some embodiments, the present disclosure provides a method ofdetermining a gene expression response signature, the method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness of anti-TNF therapy toa human interactome map; and selecting a plurality of genes selected bydiffusion state distance with one another in a human interactome map,thereby establishing the gene expression response signature.

Definitions

Administration: As used herein, the term “administration” typicallyrefers to the administration of a composition to a subject or system,for example to achieve delivery of an agent that is, or is included inor otherwise delivered by, the composition.

Agent: As used herein, the term “agent” refers to an entity (e.g., forexample, a lipid, metal, nucleic acid, polypeptide, polysaccharide,small molecule, etc., or complex, combination, mixture or system [e.g.,cell, tissue, organism] thereof), or phenomenon (e.g., heat, electriccurrent or field, magnetic force or field, etc.).

Amino acid: As used herein, the term “amino acid” refers to any compoundand/or substance that can be incorporated into a polypeptide chain,e.g., through formation of one or more peptide bonds. In someembodiments, an amino acid has the general structure H₂N—C(H)(R)—COOH.In some embodiments, an amino acid is a naturally-occurring amino acid.In some embodiments, an amino acid is a non-natural amino acid; in someembodiments, an amino acid is a D-amino acid; in some embodiments, anamino acid is an L-amino acid. As used herein, the term “standard aminoacid” refers to any of the twenty L-amino acids commonly found innaturally occurring peptides. “Nonstandard amino acid” refers to anyamino acid, other than the standard amino acids, regardless of whetherit is or can be found in a natural source. In some embodiments, an aminoacid, including a carboxy- and/or amino-terminal amino acid in apolypeptide, can contain a structural modification as compared to thegeneral structure above. For example, in some embodiments, an amino acidmay be modified by methylation, amidation, acetylation, pegylation,glycosylation, phosphorylation, and/or substitution (e.g., of the aminogroup, the carboxylic acid group, one or more protons, and/or thehydroxyl group) as compared to the general structure. In someembodiments, such modification may, for example, alter the stability orthe circulating half-life of a polypeptide containing the modified aminoacid as compared to one containing an otherwise identical unmodifiedamino acid. In some embodiments, such modification does notsignificantly alter a relevant activity of a polypeptide containing themodified amino acid, as compared to one containing an otherwiseidentical unmodified amino acid. As will be clear from context, in someembodiments, the term “amino acid” may be used to refer to a free aminoacid; in some embodiments it may be used to refer to an amino acidresidue of a polypeptide, e.g., an amino acid residue within apolypeptide.

Analog: As used herein, the term “analog” refers to a substance thatshares one or more particular structural features, elements, components,or moieties with a reference substance. Typically, an “analog” showssignificant structural similarity with the reference substance, forexample sharing a core or consensus structure, but also differs incertain discrete ways. In some embodiments, an analog is a substancethat can be generated from the reference substance, e.g., by chemicalmanipulation of the reference substance. In some embodiments, an analogis a substance that can be generated through performance of a syntheticprocess substantially similar to (e.g., sharing a plurality of stepswith) one that generates the reference substance. In some embodiments,an analog is or can be generated through performance of a syntheticprocess different from that used to generate the reference substance.

Antagonist: As used herein, the term “antagonist” may refer to an agent,or condition whose presence, level, degree, type, or form is associatedwith a decreased level or activity of a target. An antagonist mayinclude an agent of any chemical class including, for example, smallmolecules, polypeptides, nucleic acids, carbohydrates, lipids, metals,and/or any other entity that shows the relevant inhibitory activity. Insome embodiments, an antagonist may be a “direct antagonist” in that itbinds directly to its target; in some embodiments, an antagonist may bean “indirect antagonist” in that it exerts its influence by means otherthan binding directly to its target; e.g., by interacting with aregulator of the target, so that the level or activity of the target isaltered). In some embodiments, an “antagonist” may be referred to as an“inhibitor”.

Antibody: As used herein, the term “antibody” refers to a polypeptidethat includes canonical immunoglobulin sequence elements sufficient toconfer specific binding to a particular target antigen. As is known inthe art, intact antibodies as produced in nature are approximately 150kD tetrameric agents comprised of two identical heavy chain polypeptides(about 50 kD each) and two identical light chain polypeptides (about 25kD each) that associate with each other into what is commonly referredto as a “Y-shaped” structure. Each heavy chain is comprised of at leastfour domains (each about 110 amino acids long)—an amino-terminalvariable (VH) domain (located at the tips of the Y structure), followedby three constant domains: CH1, CH2, and the carboxy-terminal CH3(located at the base of the Y's stem). A short region, known as the“switch”, connects the heavy chain variable and constant regions. The“hinge” connects CH2 and CH3 domains to the rest of the antibody. Twodisulfide bonds in this hinge region connect the two heavy chainpolypeptides to one another in an intact antibody. Each light chain iscomprised of two domains—an amino-terminal variable (VL) domain,followed by a carboxy-terminal constant (CL) domain, separated from oneanother by another “switch”. Intact antibody tetramers are comprised oftwo heavy chain-light chain dimers in which the heavy and light chainsare linked to one another by a single disulfide bond; two otherdisulfide bonds connect the heavy chain hinge regions to one another, sothat the dimers are connected to one another and the tetramer is formed.Naturally-produced antibodies are also glycosylated, typically on theCH2 domain. Each domain in a natural antibody has a structurecharacterized by an “immunoglobulin fold” formed from two beta sheets(e.g., 3-, 4-, or 5-stranded sheets) packed against each other in acompressed antiparallel beta barrel. Each variable domain contains threehypervariable loops known as “complement determining regions” (CDR1,CDR2, and CDR3) and four somewhat invariant “framework” regions (FR1,FR2, FR3, and FR4). When natural antibodies fold, the FR regions formthe beta sheets that provide the structural framework for the domains,and the CDR loop regions from both the heavy and light chains arebrought together in three-dimensional space so that they create a singlehypervariable antigen binding site located at the tip of the Ystructure. The Fc region of naturally-occurring antibodies binds toelements of the complement system, and also to receptors on effectorcells, including for example effector cells that mediate cytotoxicity.As is known in the art, affinity and/or other binding attributes of Fcregions for Fc receptors can be modulated through glycosylation or othermodification. In some embodiments, antibodies produced and/or utilizedin accordance with the present invention include glycosylated Fcdomains, including Fc domains with modified or engineered suchglycosylation. For purposes of the present invention, in certainembodiments, any polypeptide or complex of polypeptides that includessufficient immunoglobulin domain sequences as found in naturalantibodies can be referred to and/or used as an “antibody”, whether suchpolypeptide is naturally produced (e.g., generated by an organismreacting to an antigen), or produced by recombinant engineering,chemical synthesis, or other artificial system or methodology. In someembodiments, an antibody is polyclonal; in some embodiments, an antibodyis monoclonal. In some embodiments, an antibody has constant regionsequences that are characteristic of mouse, rabbit, primate, or humanantibodies. In some embodiments, antibody sequence elements arehumanized, primatized, chimeric, etc, as is known in the art. Moreover,the term “antibody” as used herein, can refer in appropriate embodiments(unless otherwise stated or clear from context) to any of the art-knownor developed constructs or formats for utilizing antibody structural andfunctional features in alternative presentation. For example,embodiments, an antibody utilized in accordance with the presentinvention is in a format selected from, but not limited to, intact IgA,IgG, IgE or IgM antibodies; bi- or multi-specific antibodies (e.g.,Zybodies®, etc); antibody fragments such as Fab fragments, Fab′fragments, F(ab′)2 fragments, Fd′ fragments, Fd fragments, and isolatedCDRs or sets thereof; single chain Fvs; polypeptide-Fc fusions; singledomain antibodies (e.g., shark single domain antibodies such as IgNAR orfragments thereof); cameloid antibodies; masked antibodies (e.g.,Probodies®); Small Modular ImmunoPharmaceuticals (“SMIPs™”); singlechain or Tandem diabodies (TandAb®); VHHs; Anticalins®; Nanobodies®minibodies; BiTE®s; ankyrin repeat proteins or DARPINs®; Avimers®;DARTs; TCR-like antibodies; Adnectins®; Affilins®; Trans-Bodies®;Affibodies®; TrimerX®; MicroProteins; Fynomers®, Centyrins®; andKALBITOR®s. In some embodiments, an antibody may lack a covalentmodification (e.g., attachment of a glycan) that it would have ifproduced naturally. In some embodiments, an antibody may contain acovalent modification (e.g., attachment of a glycan, a payload [e.g., adetectable moiety, a therapeutic moiety, a catalytic moiety, etc], orother pendant group [e.g., poly-ethylene glycol, etc.]).

Associated: Two events or entities are “associated” with one another, asthat term is used herein, if the presence, level, degree, type and/orform of one is correlated with that of the other. For example, aparticular entity (e.g., polypeptide, genetic signature, metabolite,microbe, etc) is considered to be associated with a particular disease,disorder, or condition, if its presence, level and/or form correlateswith incidence of and/or susceptibility to the disease, disorder, orcondition (e.g., across a relevant population). In some embodiments, twoor more entities are physically “associated” with one another if theyinteract, directly or indirectly, so that they are and/or remain inphysical proximity with one another. In some embodiments, two or moreentities that are physically associated with one another are covalentlylinked to one another; in some embodiments, two or more entities thatare physically associated with one another are not covalently linked toone another but are non-covalently associated, for example by means ofhydrogen bonds, van der Waals interaction, hydrophobic interactions,magnetism, and combinations thereof.

Biological Sample: As used herein, the term “biological sample”typically refers to a sample obtained or derived from a biologicalsource (e.g., a tissue or organism or cell culture) of interest, asdescribed herein. In some embodiments, a source of interest comprises anorganism, such as an animal or human. In some embodiments, a biologicalsample is or comprises biological tissue or fluid. In some embodiments,a biological sample may be or comprise bone marrow; blood; blood cells;ascites; tissue or fine needle biopsy samples; cell-containing bodyfluids; free floating nucleic acids; sputum; saliva; urine;cerebrospinal fluid, peritoneal fluid; pleural fluid; feces; lymph;gynecological fluids; skin swabs; vaginal swabs; oral swabs; nasalswabs; washings or lavages such as a ductal lavages or broncheoalveolarlavages; aspirates; scrapings; bone marrow specimens; tissue biopsyspecimens; surgical specimens; feces, other body fluids, secretions,and/or excretions; and/or cells therefrom, etc. In some embodiments, abiological sample is or comprises cells obtained from an individual. Insome embodiments, obtained cells are or include cells from an individualfrom whom the sample is obtained. In some embodiments, a sample is a“primary sample” obtained directly from a source of interest by anyappropriate means. For example, in some embodiments, a primarybiological sample is obtained by methods selected from the groupconsisting of biopsy (e.g., fine needle aspiration or tissue biopsy),surgery, collection of body fluid (e.g., blood, lymph, feces etc.), etc.In some embodiments, as will be clear from context, the term “sample”refers to a preparation that is obtained by processing (e.g., byremoving one or more components of and/or by adding one or more agentsto) a primary sample. For example, filtering using a semi-permeablemembrane. Such a “processed sample” may comprise, for example nucleicacids or proteins extracted from a sample or obtained by subjecting aprimary sample to techniques such as amplification or reversetranscription of mRNA, isolation and/or purification of certaincomponents, etc.

Combination Therapy: As used herein, the term “combination therapy”refers to a clinical intervention in which a subject is simultaneouslyexposed to two or more therapeutic regimens (e.g. two or moretherapeutic agents). In some embodiments, the two or more therapeuticregimens may be administered simultaneously. In some embodiments, thetwo or more therapeutic regimens may be administered sequentially (e.g.,a first regimen administered prior to administration of any doses of asecond regimen). In some embodiments, the two or more therapeuticregimens are administered in overlapping dosing regimens. In someembodiments, administration of combination therapy may involveadministration of one or more therapeutic agents or modalities to asubject receiving the other agent(s) or modality. In some embodiments,combination therapy does not necessarily require that individual agentsbe administered together in a single composition (or even necessarily atthe same time). In some embodiments, two or more therapeutic agents ormodalities of a combination therapy are administered to a subjectseparately, e.g., in separate compositions, via separate administrationroutes (e.g., one agent orally and another agent intravenously), and/orat different time points. In some embodiments, two or more therapeuticagents may be administered together in a combination composition, oreven in a combination compound (e.g., as part of a single chemicalcomplex or covalent entity), via the same administration route, and/orat the same time.

Comparable: As used herein, the term “comparable” refers to two or moreagents, entities, situations, sets of conditions, etc., that may not beidentical to one another but that are sufficiently similar to permitcomparison there between so that one skilled in the art will appreciatethat conclusions may reasonably be drawn based on differences orsimilarities observed. In some embodiments, comparable sets ofconditions, circumstances, individuals, or populations are characterizedby a plurality of substantially identical features and one or a smallnumber of varied features. Those of ordinary skill in the art willunderstand, in context, what degree of identity is required in any givencircumstance for two or more such agents, entities, situations, sets ofconditions, etc. to be considered comparable. For example, those ofordinary skill in the art will appreciate that sets of circumstances,individuals, or populations are comparable to one another whencharacterized by a sufficient number and type of substantially identicalfeatures to warrant a reasonable conclusion that differences in resultsobtained or phenomena observed under or with different sets ofcircumstances, individuals, or populations are caused by or indicativeof the variation in those features that are varied.

Corresponding to: As used herein, the phrase “corresponding to” refersto a relationship between two entities, events, or phenomena that sharesufficient features to be reasonably comparable such that“corresponding” attributes are apparent. For example, in someembodiments, the term may be used in reference to a compound orcomposition, to designate the position and/or identity of a structuralelement in the compound or composition through comparison with anappropriate reference compound or composition. For example, in someembodiments, a monomeric residue in a polymer (e.g., an amino acidresidue in a polypeptide or a nucleic acid residue in a polynucleotide)may be identified as “corresponding to” a residue in an appropriatereference polymer. For example, those of ordinary skill will appreciatethat, for purposes of simplicity, residues in a polypeptide are oftendesignated using a canonical numbering system based on a referencerelated polypeptide, so that an amino acid “corresponding to” a residueat position 190, for example, need not actually be the 190^(th) aminoacid in a particular amino acid chain but rather corresponds to theresidue found at 190 in the reference polypeptide; those of ordinaryskill in the art readily appreciate how to identify “corresponding”amino acids. For example, those skilled in the art will be aware ofvarious sequence alignment strategies, including software programs suchas, for example, BLAST®, CS-BLAST®, CUSASW++, DIAMOND, FASTA™,GGSEARCH/GLSEARCH, Genoogle, HMMER™, HHpred/HHsearch, IDF, Infernal,KLAST, USEARCH, parasail, PSI-BLAST®, PSI-Search, ScalaBLAST®, Sequilab,SAM, SSEARCH, SWAPHI, SWAPHI-LS, SWIMM, or SWIPE that can be utilized,for example, to identify “corresponding” residues in polypeptides and/ornucleic acids in accordance with the present disclosure.

Dosing regimen: As used herein, the term “dosing regimen” refers to aset of unit doses (typically more than one) that are administeredindividually to a subject, typically separated by periods of time. Insome embodiments, a given therapeutic agent has a recommended dosingregimen, which may involve one or more doses. In some embodiments, adosing regimen comprises a plurality of doses each of which is separatedin time from other doses. In some embodiments, individual doses areseparated from one another by a time period of the same length; in someembodiments, a dosing regimen comprises a plurality of doses and atleast two different time periods separating individual doses. In someembodiments, all doses within a dosing regimen are of the same unit doseamount. In some embodiments, different doses within a dosing regimen areof different amounts. In some embodiments, a dosing regimen comprises afirst dose in a first dose amount, followed by one or more additionaldoses in a second dose amount different from the first dose amount. Insome embodiments, a dosing regimen comprises a first dose in a firstdose amount, followed by one or more additional doses in a second doseamount same as the first dose amount. In some embodiments, a dosingregimen is correlated with a desired or beneficial outcome whenadministered across a relevant population (i.e., is a therapeutic dosingregimen).

Improved, increased or reduced: As used herein, the terms “improved,”“increased,” or “reduced,”, or grammatically comparable comparativeterms thereof, indicate values that are relative to a comparablereference measurement. For example, in some embodiments, an assessedvalue achieved with an agent of interest may be “improved” relative tothat obtained with a comparable reference agent. Alternatively oradditionally, in some embodiments, an assessed value achieved in asubject or system of interest may be “improved” relative to thatobtained in the same subject or system under different conditions (e.g.,prior to or after an event such as administration of an agent ofinterest), or in a different, comparable subject (e.g., in a comparablesubject or system that differs from the subject or system of interest inpresence of one or more indicators of a particular disease, disorder orcondition of interest, or in prior exposure to a condition or agent,etc.).

Pharmaceutical composition: As used herein, the term “pharmaceuticalcomposition” refers to an active agent, formulated together with one ormore pharmaceutically acceptable carriers. In some embodiments, theactive agent is present in unit dose amounts appropriate foradministration in a therapeutic regimen to a relevant subject (e.g., inamounts that have been demonstrated to show a statistically significantprobability of achieving a predetermined therapeutic effect whenadministered), or in a different, comparable subject (e.g., in acomparable subject or system that differs from the subject or system ofinterest in presence of one or more indicators of a particular disease,disorder or condition of interest, or in prior exposure to a conditionor agent, etc.). In some embodiments, comparative terms refer tostatistically relevant differences (e.g., that are of a prevalenceand/or magnitude sufficient to achieve statistical relevance). Thoseskilled in the art will be aware, or will readily be able to determine,in a given context, a degree and/or prevalence of difference that isrequired or sufficient to achieve such statistical significance.

Pharmaceutically acceptable: As used herein, the phrase“pharmaceutically acceptable” refers to those compounds, materials,compositions, and/or dosage forms which are, within the scope of soundmedical judgment, suitable for use in contact with the tissues of humanbeings and animals without excessive toxicity, irritation, allergicresponse, or other problem or complication, commensurate with areasonable benefit/risk ratio.

Primary Non-Responder: As used herein, the term “primary non-responder”refers to a subject that displays a lack of improvement in clinicalsigns and symptoms after receiving anti-TNF therapy for a period oftime. Those skilled in the art will understand that the medicalcommunity may establish an appropriate period of time for any particulardisease or condition, or for any particular patient or patient type. Togive but a few examples, in some embodiments, the period of time may beat least 8 weeks. In some embodiments, the period of time may be atleast 12 weeks. In some embodiments, the period of time may be 14 weeks.

Reference: As used herein, the term “reference” describes a standard orcontrol relative to which a comparison is performed. For example, insome embodiments, an agent, animal, individual, population, sample,sequence or value of interest is compared with a reference or controlagent, animal, individual, population, sample, sequence or value. Insome embodiments, a reference or control is tested and/or determinedsubstantially simultaneously with the testing or determination ofinterest. In some embodiments, a reference or control is a historicalreference or control, optionally embodied in a tangible medium.Typically, as would be understood by those skilled in the art, areference or control is determined or characterized under comparableconditions or circumstances to those under assessment. Those skilled inthe art will appreciate when sufficient similarities are present tojustify reliance on and/or comparison to a particular possible referenceor control.

Secondary Non-Responder: As used herein, the term “secondarynon-responder” refers to a subject that displays an initial improvementin clinical signs or symptoms after receiving anti-TNF therapy, but showa statistically significant decrease in such improvement over time.

Therapeutically effective amount: As used herein, the term“therapeutically effective amount” refers to an amount of a substance(e.g., a therapeutic agent, composition, and/or formulation) thatelicits a desired biological response when administered as part of atherapeutic regimen. In some embodiments, a therapeutically effectiveamount of a substance is an amount that is sufficient, when administeredto a subject suffering from or susceptible to a disease, disorder,and/or condition, to treat, diagnose, prevent, and/or delay the onset ofthe disease, disorder, and/or condition. As will be appreciated by thoseof ordinary skill in this art, the effective amount of a substance mayvary depending on such factors as the desired biological endpoint, thesubstance to be delivered, the target cell or tissue, etc. For example,the effective amount of compound in a formulation to treat a disease,disorder, and/or condition is the amount that alleviates, ameliorates,relieves, inhibits, prevents, delays onset of, reduces severity ofand/or reduces incidence of one or more symptoms or features of thedisease, disorder and/or condition. In some embodiments, atherapeutically effective amount is administered in a single dose; insome embodiments, multiple unit doses are required to deliver atherapeutically effective amount.

Variant: As used herein, the term “variant” refers to an entity thatshows significant structural identity with a reference entity butdiffers structurally from the reference entity in the presence or levelof one or more chemical moieties as compared with the reference entity.In many embodiments, a variant also differs functionally from itsreference entity. In general, whether a particular entity is properlyconsidered to be a “variant” of a reference entity is based on itsdegree of structural identity with the reference entity. As will beappreciated by those skilled in the art, any biological or chemicalreference entity has certain characteristic structural elements. Avariant, by definition, is a distinct chemical entity that shares one ormore such characteristic structural elements. To give but a fewexamples, a small molecule may have a characteristic core structuralelement (e.g., a macrocycle core) and/or one or more characteristicpendent moieties so that a variant of the small molecule is one thatshares the core structural element and the characteristic pendentmoieties but differs in other pendent moieties and/or in types of bondspresent (single vs double, E vs Z, etc.) within the core, a polypeptidemay have a characteristic sequence element comprised of a plurality ofamino acids having designated positions relative to one another inlinear or three-dimensional space and/or contributing to a particularbiological function, a nucleic acid may have a characteristic sequenceelement comprised of a plurality of nucleotide residues havingdesignated positions relative to on another in linear orthree-dimensional space. For example, a variant polypeptide may differfrom a reference polypeptide as a result of one or more differences inamino acid sequence and/or one or more differences in chemical moieties(e.g., carbohydrates, lipids, etc.) covalently attached to thepolypeptide backbone. In some embodiments, a variant polypeptide showsan overall sequence identity with a reference polypeptide that is atleast 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,or 99%. Alternatively or additionally, in some embodiments, a variantpolypeptide does not share at least one characteristic sequence elementwith a reference polypeptide. In some embodiments, the referencepolypeptide has one or more biological activities. In some embodiments,a variant polypeptide shares one or more of the biological activities ofthe reference polypeptide. In some embodiments, a variant polypeptidelacks one or more of the biological activities of the referencepolypeptide. In some embodiments, a variant polypeptide shows a reducedlevel of one or more biological activities as compared with thereference polypeptide. In many embodiments, a polypeptide of interest isconsidered to be a “variant” of a parent or reference polypeptide if thepolypeptide of interest has an amino acid sequence that is identical tothat of the parent but for a small number of sequence alterations atparticular positions. Typically, fewer than 20%, 15%, 10%, 9%, 8%, 7%,6%, 5%, 4%, 3%, 2% of the residues in the variant are substituted ascompared with the parent. In some embodiments, a variant has 10, 9, 8,7, 6, 5, 4, 3, 2, or 1 substituted residue as compared with a parent.Often, a variant has a very small number (e.g., fewer than 5, 4, 3, 2,or 1) number of substituted functional residues (i.e., residues thatparticipate in a particular biological activity). Furthermore, a varianttypically has not more than 5, 4, 3, 2, or 1 additions or deletions, andoften has no additions or deletions, as compared with the parent.Moreover, any additions or deletions are typically fewer than about 25,about 20, about 19, about 18, about 17, about 16, about 15, about 14,about 13, about 10, about 9, about 8, about 7, about 6, and commonly arefewer than about 5, about 4, about 3, or about 2 residues. In someembodiments, the parent or reference polypeptide is one found in nature.

BRIEF DESCRIPTION OF THE DRAWING

FIGS. 1A and 1B are plots illustrating ulcerative colitis (UC) responsesignature genes modules detected using the human interactome (HI) fromthe UC cohort. The response signature genes found in gene expressiondata form a significant cluster when mapped to the HI (FIG. 1A) and ismuch larger than expected by chance (FIG. 1B) which reflects anunderlying biology of response.

FIGS. 2A and 2B are plots illustrating in-cohort performance of responsepredictions of a near perfect classifier using leave-one-outcross-validation. FIG. 2A is a receiver operating characteristic (ROC)curve and FIG. 2B illustrates the Negative Predictive Value (NPV) vs.True Negative Rate (TNR) curve. The classifier is able to detect 70% ofthe non-responders with 100% accuracy, and 100% of the non-responderswith 90% accuracy.

FIGS. 3A and 3B are plots illustrating cross-cohort performance ofresponse prediction classifier when testing on an independent cohort.FIG. 3A is an ROC curve and FIG. 3B illustrates the NPV vs. TNR curve.The classifier is able to detect 50% of the non-responders with 100%accuracy.

FIGS. 4A, 4B, 4C, and 4D are plots illustrating in-cohort rheumatoidarthritis (RA) classifier validation using leave-one-out crossvalidation when training on Feature Set 1 (FIGS. 4A and 4B) and top ninesignature genes (FIGS. 4C and 4D).

FIGS. 5A and 5B are plots illustrating ROC curves of cross cohortclassifier test results (in FIG. 5A) and negative predictive performance(in FIG. 5B) for the RA classifier.

FIG. 6 is an exemplary workflow for developing a classifier.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

As noted, the response rate for patients undergoing anti-TNF therapy isinconsistent. Technologies that reliably identify responsive ornon-responsive subjects would be beneficial, as they would avoidwasteful and even potentially damaging administration of therapy tosubjects who will not respond, and furthermore would allow timelydetermination of more appropriate treatment for such subjects. Thepresent disclosure provides such technologies, addressing needs ofpatients, their families, drug developers, and medical professionalseach of whom suffers under the current system.

While significant effort has been invested in efforts to developtechnologies that reliably predict responsiveness (e.g., by identifyingresponsive vs. non-responsive populations) or development of resistancefor certain therapeutic agents, regimens, or modalities, success hasbeen elusive, and almost exclusively limited to the oncology sector.Complex disorders, such as autoimmune and/or cardiovascular diseaseshave proven to be particularly challenging.

Cancer is typically associated with particular strong driver genes,which dramatically simplifies the analysis required to identifyresponder vs non-responder patient populations, and significantlyimproves success rates. By contrast, diseases associated with morecomplex genetic (and/or epigenetic) contributions, have thus farpresented an insurmountable challenge for available technologies.

Indeed, a large number of published reports describe efforts to developtechnologies for predicting responsiveness to anti-TNF therapy ininflammatory conditions (e.g., rheumatoid arthritis), most commonlyrelying on blood-based gene expression classifiers. See, e.g., Nakamuraet al. “Identification of baseline gene expression signatures predictingtherapeutic responses to three biologic agents in rheumatoid arthritis:a retrospective observational study” Arthritis Research & Therapy (2016)18:159 DOI 10.1186/s13075-016-1052-8. However, a clinically utilizableclassifier has not yet been identified. Notably, Toonen et al. performedan independent study that tested eight different gene expressionsignatures predicting response to anti-TNF, and reported that mostsignatures failed to demonstrate sufficient predictive value to be ofutility. See M. Toonen et al., “Validation Study of Existing GeneExpression Signatures for Anti-TNF Treatment in Patients with RheumatoidArthritis,” PLOS ONE 7(3): e33199. Thomson et al. attempted to describea blood-based classifier to identify non-responders to one anti-TNFtherapy, infliximab, in rheumatoid arthritis. Thomson et al.,“Blood-based identification of non-responders to anti-TNF therapy inrheumatoid arthritis,” BMC Med Genomics, 8:26, *142 (2015). Theirproposed classifier comprised 18 signaling mechanisms indicative ofhigher TNF-mediated inflammatory signaling in responders at baseline,versus higher levels of specific metabolic activities in non-respondersat baseline. The test, however, did not reach the level of predictiveaccuracy required for commercialization and so development was stopped.

Typically, conventional strategies for defining responder vs.non-responder classifiers for anti-TNF therapy rely on machine-learningapproaches, using mean values across classes of response, and focusingon genes with the highest fold changes, often in a pathway-basedcontext. The present disclosure identifies various sources of problemswith these conventional approaches, and, moreover, provides technologiesthat solve or avoid the problems, thereby satisfying the long felt needwithin the community for accurate and/or useful predictive classifiers.

Among other things, the present disclosure appreciates that machinelearning may be useful for finding correlation between datasets ofpatients, but fails to achieve sufficient predictive accuracy acrosscohorts. Furthermore, the present disclosure identifies thatprioritizing or otherwise focusing on highest fold changes misses subtlebut meaningful differences relevant to disease biology. Still further,the present disclosure offers an insight that mapping of genes withaltered expression levels onto a human interactome (e.g., thatrepresents experimentally supported physical interactions betweencellular components and, in some embodiments, explicitly excludes anytheoretical, calculated, or other interaction that has been proposed butnot experimentally validated) can provide a useful and effectiveclassifier for defining responders vs. non-responders to anti-TNFtherapy. In some embodiments, genes included in such a classifierrepresent a connected module in the human interactome.

Anti-TNF Therapy

TNF-mediated disorders are currently treated by inhibition of TNF, andin particular by administration of an anti-TNF agent (i.e., by anti-TNFtherapy). Examples of anti-TNF agents approved for use in the UnitedStates include monoclonal antibodies such as adalimumab (Humira®),certolizumab pegol (Cimiza®), infliximab (Remicade®), and decoycirculating receptor fusion proteins such as etanercept (Enbrel®). Theseagents are currently approved for use in treatment of indications,according to dosing regimens, as set forth below in Table 1:

TABLE 1 Indication Adalimumab¹ Certolizumab Pegol¹ Infliximab²Etanercept¹ Golimumab¹ Golimumab² Juvenile 10 kg (22 lbs) N/A N/A 0.8mg/kg weekly, N/A N/A Idiopathic to <15 kg (33 lbs): with a maximum ofArthritis 10 mg every 50 mg per week other week 15 kg (33 lbs) to <30 kg(66 lbs): 20 mg every other week ≥30 kg (66 lbs): 40 mg every other weekPsoriatic 40 mg every other 400 mg initially and 5 mg/kg at 0, 2 and 650 mg once weekly 50 mg administered N/A Arthritis week at week 2 and 4,weeks, then every 8 with or without by subcutaneous followed by 200 mgweeks methotrexate injection once a every other week; for monthmaintenance dosing, 400 mg every 4 weeks Rheumatoid 40 mg every other400 mg initially and In conjunction with 50 mg once weekly 50 mg once amonth 2 mg/kg intravenous Arthritis week at Weeks 2 and 4, methotrexate,3 with or without infusion over 30 followed by 200 mg mg/kg at 0, 2 and6 methotrexate minutes at weeks 0 every other week; for weeks, thenevery 8 and 4, then every 8 maintenance dosing, weeks weeks 400 mg every4 weeks Ankylosing 40 mg every other 400 mg (given as 2 5 mg/kg at 0, 2and 6 50 mg once weekly 50 mg administered N/A Spondylitis weeksubcutaneous weeks, then every 6 by subcutaneous injections of 200 mgweeks injection once a each) initially and at month weeks 2 and 4,followed by 200 mg every other week or 400 mg every 4 weeks AdultInitial dose (Day 400 mg initially 5 mg/kg at 0, 2 and 6 N/A N/A N/ACrohn's 1): 160 mg and at Weeks 2 weeks, then every 8 Disease Seconddose two and 4 weeks. weeks later (Day Continue with 15): 80 mg 400 mgevery Two weeks four weeks later (Day 29): Begin a maintenance dose of40 mg every other week Pediatric 17 kg (37 lbs) N/A 5 mg/kg at 0, 2 and6 N/A N/A N/A Crohn's to <40 kg (88 lbs): weeks, then every 8 DiseaseInitial dose (Day weeks. 1): 80 mg Second dose two weeks later (Day 15):40 mg Two weeks later (Day 29): Begin a maintenance dose of 20 mg everyother week ≥40 kg (88 lbs): Initial dose (Day 1): 160 mg Second dose twoweeks later (Day 15): 80 mg Two weeks later (Day 29): Begin amaintenance dose of 40 mg every other week Ulcerative Initial dose (DayN/A 5 mg/kg at 0, 2 and 6 N/A N/A N/A Colitis 1): 160 mg weeks, thenevery 8 Second dose two weeks. weeks later (Day 15): 80 mg Two weekslater (Day 29): Begin a maintenance dose of 40 mg every other weekPlaque 80 mg initial dose; 40 N/A N/A 50 mg twice weekly N/A N/APsoriasis mg every other week for 3 months, beginning one week followedby 50 mg after initial dose once weekly Hidradenitis Initial dose (DayN/A N/A N/A N/A N/A Suppurativa 1): 160 mg Second dose two weeks later(Day 15): 80 mg Third dose (Day 29) and subsequent doses: 40 mg everyweek Uveitis 80 mg initial dose; 40 N/A N/A N/A N/A N/A mg every otherweek beginning one week after initial dose ¹Administered by subcutaneousinjection. ²Administered by intravenous infusion.

The present disclosure provides technologies relevant to anti-TNFtherapy, including those therapeutic regimens as set forth in Table 1.In some embodiments, the anti-TNF therapy is or comprises administrationof infliximab (Remicade®), adalimumab (Humira®), certolizumab pegol(Cimiza®), etanercept (Enbel®), or biosimilars thereof. In someembodiments, the anti-TNF therapy is or comprises administration ofinfliximab (Remicade®) or adalimumab (Humira®). In some embodiments, theanti-TNF therapy is or comprises administration of infliximab(Remicade®). In some embodiments, the anti-TNF therapy is or comprisesadministration of adalimumab (Humira®).

In some embodiments, the anti-TNF therapy is or comprises administrationof a biosimilar anti-TNF agent. In some embodiments, the anti-TNF agentis selected from infliximab biosimilars such as CT-P13, BOW015, SB2,Inflectra, Renflexis, and Ixifi, adalimumab biosimilars such as ABP 501(AMGEVITA™), Adfrar, and Hulin™ and etanercept biosimilars such asHD203, SB4 (Benepali®), GP2015, Erelzi, and Intacept.

In some embodiments, the present disclosure defines patient populationsto whom anti-TNF therapy should (or should not) be administered. In someembodiments, technologies provided by the present disclosure generateinformation useful to doctors, pharmaceutical companies, payers, and/orregulatory agencies who wish to ensure that anti-TNF therapy isadministered to responder populations and/or is not administered tonon-responder populations.

Diseases, Disorders or Conditions

In general, provided disclosures are useful in any context in whichadministration of anti-TNF therapy is contemplated or implemented. Insome embodiments, provided technologies are useful in the diagnosisand/or treatment of subjects suffering from a disease, disorder, orcondition associated with aberrant (e.g., elevated) TNF expressionand/or activity. In some embodiments, provided technologies are usefulin monitoring subjects who are receiving or have received anti-TNFtherapy. In some embodiments, provided technologies identify whether asubject will or will not respond to a given anti-TNF therapy. In someembodiments, the provided technologies identify whether a subject willdevelop resistance to a given anti-TNF therapy.

Accordingly, the present disclosure provides technologies relevant totreatment of the various disorders related to TNF, including thoselisted in Table 1. In some embodiments, a subject is suffering from adisease, disorder, or condition selected from rheumatoid arthritis,psoriatic arthritis, ankylosing spondylitis, Crohn's disease (adult orpediatric), ulcerative colitis, inflammatory bowel disease, chronicpsoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis,and juvenile idiopathic arthritis. In some embodiments, the disease,disorder, or condition is rheumatoid arthritis. In some embodiments, thedisease, disorder, or condition is psoriatic arthritis. In someembodiments, the disease, disorder, or condition is ankylosingspondylitis. In some embodiments, the disease, disorder, or condition isCrohn's disease. In some embodiments, the disease, disorder, orcondition is adult Crohn's disease. In some embodiments, the disease,disorder, or condition is pediatric Crohn's disease. In someembodiments, the disease, disorder, or condition is inflammatory boweldisease. In some embodiments, the disease, disorder, or condition isulcerative colitis. In some embodiments, the disease, disorder, orcondition is chronic psoriasis. In some embodiments, the disease,disorder, or condition is plaque psoriasis. In some embodiments, thedisease, disorder, or condition is hidradenitis suppurativa. In someembodiments, the disease, disorder, or condition is asthma. In someembodiments, the disease, disorder, or condition is uveitis. In someembodiments, the disease, disorder, or condition is juvenile idiopathicarthritis.

Provided Classifier(s)

The present disclosure provides gene expression response signatures thatserve as gene classifiers and identify (i.e., predict) which patientswill or will not respond to anti-TNF therapy. That is, the presentdisclosure provides methods of determining gene expression responsesignatures that are characteristic of anti-TNF responder ornon-responder populations. In some embodiments, a particular geneexpression response signature classifies responder or non-responderpopulations for a particular anti-TNF therapy (e.g., a particularanti-TNF agent and/or regimen). In some embodiments, responder and/ornon-responder populations for different anti-TNF therapies (e.g.,different anti-TNF agents and/or regimens) may overlap or beco-extensive; in some such embodiments, the present disclosure mayprovide gene expression response signatures that serve as geneclassifiers for responder and/or non-responder populations acrossanti-TNF therapies.

In some embodiments, as described herein, a gene expression responsesignature is identified by retrospective analysis of gene expressionlevels in biological samples from patients who have received anti-TNFtherapy and have been determined to respond (i.e., are responders) ornot to respond (i.e., are non-responders). In some embodiments, all suchpatients have received the same anti-TNF therapy (optionally for thesame or different periods of time); alternatively or additionally, insome embodiments, all such patients have been diagnosed with the samedisease, disorder or condition. In some embodiments, patients whosebiological samples are analyzed in the retrospective analysis hadreceived different anti-TNF therapy (e.g., with a different anti-TNFagent and/or according to a different regimen); alternatively oradditionally, in some embodiments, patients whose biological samples areanalyzed in the retrospective analysis have been diagnosed withdifferent diseases, disorders, or conditions.

Typically, a gene expression response signature as described herein isdetermined by comparison of gene expression levels in the responder vs.non-responder populations whose biological samples are analyzed in aretrospective analysis as described herein. Genes whose expressionlevels show statistically significant differences between the responderand non-responder populations may be included in the gene responsesignature.

In some embodiments, the present disclosure embodies an insight that thesource of a problem with certain prior efforts to identify or providegene expression response signatures through comparison of geneexpression levels in responder vs non-responder populations haveemphasized and/or focused on (often solely on) genes that show thelargest difference (e.g., greater than 2-fold change) in expressionlevels between the populations. The present disclosure appreciates thateven genes those expression level differences are relatively small(e.g., less than 2-fold change in expression) provide useful informationand are valuably included in a gene expression response signature inembodiments described herein.

Moreover, in some embodiments, the present disclosure embodies aninsight that analysis of interaction patterns of genes whose expressionlevels show statistically significant differences (optionally includingsmall differences) between responder and non-responder populations asdescribed herein provides new and valuable information that materiallyimproves the quality and predictive power of a gene expression responsesignature.

Further, as noted, the present disclosure provides technologies thatallow practitioners to reliably and consistently predict response in acohort of subjects. In particular, for example, the rate of response forsome anti-TNF therapies is less than 35% within a given cohort ofsubjects. The provided technologies allow for prediction of greater than65% accuracy within a cohort of subjects a response rate (i.e., whethercertain subjects will or will not respond to a given therapy). In someembodiments, the methods and systems described herein predict 65% orgreater the subjects that are responders (i.e., will respond to anti-TNFtherapy) within a given cohort. In some embodiments, the methods andsystems described herein predict 70% or greater the subjects that areresponders within a given cohort. In some embodiments, the methods andsystems described herein predict 80% or greater the subjects that areresponders within a given cohort. In some embodiments, the methods andsystems described herein predict 90% or greater the subjects that areresponders within a given cohort. In some embodiments, the methods andsystems described herein predict 100% the subjects that are responderswithin a given cohort. In some embodiments, the methods and systemsdescribed herein predict 65% or greater the subjects that arenon-responders (i.e., will not respond to anti-TNF therapy) within agiven cohort. In some embodiments, the methods and systems describedherein predict 70% or greater the subjects that are non-responderswithin a given cohort. In some embodiments, the methods and systemsdescribed herein predict 80% or greater the subjects that arenon-responders within a given cohort. In some embodiments, the methodsand systems described herein predict 90% or greater the subjects thatare non-responders within a given cohort. In some embodiments, themethods and systems described herein predict 100% of the subjects thatare non-responders within a given cohort.

Defining Classifier(s)

A provided gene expression response signature (i.e., a gene classifier)is a gene or set of genes that can be used to determine whether asubject will or will not respond to a particular therapy (e.g., anti-TNFtherapy). In some embodiments, a gene expression response signature canbe identified using mRNA and/or protein expression datasets, for exampleas may be or have been prepared from validated biological data (e.g.,biological data derived from publicly available databases such as GeneExpression Omnibus (“GEO”)). In some embodiments, a gene expressionresponse signature may be derived by comparing gene expression levels ofknown responsive and known non-responsive prior subjects to a specifictherapy (e.g., anti-TNF therapy). In some embodiments, certain genes(i.e., signature genes) are selected from this cohort of gene expressiondata to be used in developing the gene expression response signature.

In some embodiments, signature genes are identified by methods analogousto those reported by Santolini, “A personalized, multiomics approachidentifies genes involved in cardiac hypertrophy and heart failure,”Systems Biology and Applications, (2018)4:12;doi:10.1038/s41540-018-0046-3, which is incorporated herein byreference. In some embodiments, signature genes are identified bycomparing gene expression levels of known responsive and non-responsiveprior subjects and identifying significant changes between the twogroups, wherein the significant changes can be large differences inexpression (e.g., greater than 2-fold change), small differences inexpression (e.g., less than 2-fold change), or both. In someembodiments, genes are ranked by significance of difference inexpression. In some embodiments, significance is measured by Pearsoncorrelation between gene expression and response outcome. In someembodiments, signature genes are selected from the ranking bysignificance of difference in expression. In some embodiments, thenumber of signature genes selected is less than the total number ofgenes analyzed. In some embodiments, 200 signature genes or less areselected. In some embodiments 100 genes or less are selected.

In some embodiments, signature genes are selected in conjunction withtheir location on a human interactome (HI), a map of protein-proteininteractions. Use of the HI in this way encompasses a recognition thatmRNA activity is dynamic and determines the actual over and underexpression of proteins critical to understanding certain diseases. Insome embodiments, genes associated with response to certain therapies(i.e., anti-TNF therapy) may cluster (i.e., form a cluster of genes) indiscrete modules on the HI map. The existence of such clusters isassociated with the existence of fundamental underlying disease biology.In some embodiments, a gene expression response signature is derivedfrom signature genes selected from the cluster of genes on the HI map.Accordingly, in some embodiments, a gene expression response signatureis derived from a cluster of genes associated with response to anti-TNFtherapy on a human interactome map.

In some embodiments, genes associated with response to certain therapiesexhibit certain topological properties when mapped onto a humaninteractome map. For example, in some embodiments, a plurality of genesassociated with response to anti-TNF therapy and characterized by theirposition (i.e., topological properties, e.g., their proximity to oneanother) on a human interactome map.

In some embodiments, genes associated with response to certain therapies(i.e., anti-TNF therapy) may exist within close proximity to one anotheron the HI map. Said proximal genes, do not necessarily need to sharefundamental underlying disease biology. That is, in some embodiments,proximal genes do not share significant protein interaction.Accordingly, in some embodiments, the gene expression response signatureis derived from genes that are proximal on a human interactome map. Insome embodiments, the gene expression response signature is derived fromcertain other topological features on a human interactome map.

In some embodiments, genes associated with response to certain therapies(i.e., anti-TNF therapy) may be determined by Diffusion State Distance(DSD) (see Cao, et al., PLOS One, 8(10): e76339 (Oct. 23, 2013)) whenused in combination with the HI map.

In some embodiments, signature genes are selected by (1) ranking genesbased on the significance of difference of expression of genes ascompared to known responders and known non-responders; (2) selectinggenes from the ranked genes and mapping the selected genes onto a humaninteractome map; and (3) selecting signature genes from the genes mappedonto the human interactome map.

In some embodiments, signature genes (e.g., selected from the Santolinimethod, or using various network topological properties including, butnot limited to, clustering, proximity and diffusion-based methods) areprovided to a probabilistic neural network to thereby provide (i.e.,“train”) the gene expression response signature. In some embodiments,the probabilistic neural network implements the algorithm proposed by D.F. Specht in “Probabilistic Neural Networks,” Neural Networks,3(1):109-118 (1990), which is incorporated herein by reference. In someembodiments, the probabilistic neural network is written in theR-statistical language, and knowing a set of observations described by avector of quantitative variables, classifies observations into a givennumber of groups (e.g., responders and non-responders). The algorithm istrained with the data set of signature genes taken from known respondersand non-responders and guesses new observations that are provided. Insome embodiments, the probabilistic neural network is one derived fromCRAN.R-project.org.

Alternatively or additionally, in some embodiments, a gene expressionresponse signature can be trained in the probabilistic neural networkusing a cohort of known responders and non-responders usingleave-one-out cross and/or k-fold cross validation. In some embodiments,such a process leaves one sample out (i.e., leave-one-out) of theanalysis and trains the classifier only based on the remaining samples.In some embodiments, the updated classifier is then used to predict aprobability of response for the sample that's left out. In someembodiments, such a process can be repeated iteratively, for example,until all samples have been left out once. In some embodiments, such aprocess randomly partitions a cohort of known responders andnon-responders into k equal sizes groups. Of the k groups, a singlegroup is retained as validation data for testing the model, and theremaining groups are used as training data. Such a process can berepeated k times, with each of the k groups being used exactly once asthe validation data. In some embodiments, the outcome is a probabilityscore for each sample in the training set. Such probability scores cancorrelate with actual response outcome. A Recursive Operating Curves(ROC) can be used to estimate the performance of the classifier. In someembodiments, an Area Under Curve (AUC) of about 0.6 or higher reflects asuitable validated classifier. In some embodiments, a NegativePredictive Value (NPV) of 0.9 reflects a suitable validated classifier.In some embodiments, a classifier can be tested in a completelyindependent (i.e., blinded) cohort to, for example, confirm thesuitability (i.e., using leave-one-out and/or k-fold cross validation).Accordingly, in some embodiments, provided methods further comprise oneor more steps of validating a gene expression response signature, forexample, by assigning probability of response to a group of knownresponders and non-responders; and checking the gene expression responsesignature against a blinded group of responders and non-responders. Theoutput of these processes is a trained gene expression responsesignature useful for establishing whether a subject will or will notrespond to a particular therapy (e.g., anti-TNF therapy).

Accordingly, in some embodiments, the gene expression response signatureis established to distinguish between responsive and non-responsiveprior subjects who have received a type of therapy, e.g., anti-TNFtherapy. This gene expression response signature, derived from theseprior responders and non-responders, is used to classify subjects(outside of the previously-identify cohorts) as responders ornon-responders, i.e., can predict whether a subject will or will notrespond to a given therapy. In some embodiments, the response andnon-responsive prior subjects suffered from the same disease, disorder,or condition.

Detecting Classifier(s)

Detecting gene classifiers in subjects, once the gene classifier isidentified, is a routine matter for those of skill in the art. In otherwords, by first defining the gene classifier, a variety of methods canbe used to determine whether a subject or group of subjects express theestablished gene classifier. For example, in some embodiments, apractitioner can obtain a blood or tissue sample from the subject priorto administering of therapy, and extract and analyze mRNA profiles fromsaid blood or tissue sample. The analysis of mRNA profiles can beperformed by any method known to those of skill in the art, including,but not limited gene arrays, RNA-sequencing, nanostring sequencing,real-time quantitative reverse transcription PCR (qRT-PCR), bead arrays,or enzyme-linked immunosorbent assay (ELISA). Accordingly, in someembodiments, the present disclosure provides methods of determiningwhether a subject is classified as a responder or non-responder,comprising measuring gene expression by at least one of a microarray,RNA sequencing, real-time quantitative reverse transcription PCR(qRT-PCR), bead array, and ELISA. In some embodiments, the presentdisclosure provides methods of determining whether a subject isclassified as a responder or non-responder comprising measuring geneexpression of a subject by RNA sequencing (i.e., RNAseq).

In some embodiments, the provided technologies provide methodscomprising determining, prior to administering anti-TNF therapy, that asubject displays a gene expression response signature associated withresponse to anti-TNF therapy; and administering the anti-TNF therapy tothe subject determined to display the gene expression responsesignature. In some embodiments, the provided technologies providemethods comprising determining, prior to administering anti-TNF therapy,that a subject does not display the gene expression response signature;and administering a therapy alternative to anti-TNF therapy to thesubject determine not to display the gene expression signature.

In some embodiments, the therapy alternative to anti-TNF therapy isselected from rituximab (Rituxan®), sarilumab (Kevzara®), tofacitinibcitrate (Xeljanz®), lefunomide (Arava®), vedolizumab (Entyvio®),tocilizumab (Actemra®), anakinra (Kineret®), and abatacept (Orencia®).

In some embodiments, gene expression is measured by subtractingbackground data, correcting for batch effects, and dividing by meanexpression of housekeeping genes. See Eisenberg & Levanon, “Humanhousekeeping genes, revisited,” Trends in Genetics, 29(10):569-574(October 2013). In the context of microarray data analysis, backgroundsubtraction refers to subtracting the average fluorescent signal arisingfrom probe features on a chip not complimentary to any mRNA sequence,i.e. signals that arise from non-specific binding, from the fluorescencesignal intensity of each probe feature. The background subtraction canbe performed with different software packages, such as Affymetrix™ GeneExpression Console. Housekeeping genes are involved in basic cellmaintenance and, therefore, are expected to maintain constant expressionlevels in all cells and conditions. The expression level of genes ofinterest, i.e., those in the response signature, can be normalized bydividing the expression level by the average expression level across agroup of selected housekeeping genes. This housekeeping genenormalization procedure calibrates the gene expression level forexperimental variability. Further, normalization methods such as robustmulti-array average (“RMA”) correct for variability across differentbatches of microarrays, are available in R packages recommended byeither Illumina™ and/or Affymetrix™ platforms. The normalized data islog transformed, and probes with low detection rates across samples areremoved. Furthermore, probes with no available genes symbol or Entrez IDare removed from the analysis.

In some embodiments, the present disclosure provides a kit comprising agene expression response signature established to distinguish betweenresponsive and non-responsive prior subjects who have received anti-TNFtherapy. In some embodiments, the kit compares levels of gene expressionof a subject to the gene expression response signature (i.e., the geneclassifier) established to distinguish between responsive andnon-responsive prior subjects who have received anti-TNF therapy.

Using Classifiers

Patient Stratification

Among other things, the present disclosure provides technologies forpredicting responsiveness to anti-TNF therapies. In some embodiments,provided technologies exhibit consistency and/or accuracy across cohortssuperior to previous methodologies.

Thus, the present disclosure provides technologies for patientstratification, defining and/or distinguishing between responder andnon-responder populations. For example, in some embodiments, the presentdisclosure provides methods for treating subjects with anti-TNF therapy,which methods, in some embodiments, comprise a step of: administeringthe anti-TNF therapy to subjects who have been determined to display agene expression response signature established to distinguish betweenresponsive and non-responsive prior subjects who have received theanti-TNF therapy. In some such embodiments, the gene expression responsesignature includes a plurality of genes established to distinguishbetween responsive and non-responsive prior subjects for a givenanti-TNF therapy. In some embodiments, the plurality of genes aredetermined to cluster with one another in a human interactome map. Insome embodiments, the plurality of genes are proximal in a humaninteractome map. In some embodiments, the plurality of genes comprisegenes that are shown to be statistically significantly different betweenresponsive and non-responsive prior subjects.

Therapy Monitoring

Further, the present disclosure provides technologies for monitoringtherapy for a given subject or cohort of subjects. As a subject's geneexpression level can change over time, it may, in some instances, benecessary or desirable to evaluate a subject at one or more points intime, for example, at specified and or periodic intervals.

In some embodiments, repeated monitoring under time permits or achievesdetection of one or more changes in a subject's gene expression profileor characteristics that may impact ongoing treatment regimens. In someembodiments, a change is detected in response to which particulartherapy administered to the subject is continued, is altered, or issuspended. In some embodiments, therapy may be altered, for example, byincreasing or decreasing frequency and/or amount of administration ofone or more agents or treatments with which the subject is already beingtreated. Alternatively or additionally, in some embodiments, therapy maybe altered by addition of therapy with one or more new agents ortreatments. In some embodiments, therapy may be altered by suspension orcessation of one or more particular agents or treatments.

To give but one example, if a subject is initially classified asresponsive (because the subject's gene expression correlated to a geneexpression response signature associated with a disease, disorder, orcondition), a given anti-TNF therapy can then be administered. At agiven interval (e.g., every six months, every year, etc.), the subjectcan be tested again to ensure that they still qualify as “responsive” toa given anti-TNF therapy. In the event the gene expression levels for agiven subject change over time, and the subject no longer expressesgenes associated with the gene expression response signature, or nowexpresses genes associated with non-responsiveness, the subject'stherapy can be altered to suit the change in gene expression.

Accordingly, in some embodiments, the present disclosure providesmethods of administering therapy to a subject previously determined todisplay a gene expression response signature associated with anti-TNFtherapy, wherein the subject displays a gene expression responsesignature associated with response to anti-TNF therapy.

In some embodiments, the present disclosure provides methods of treatingsubjects with anti-TNF therapy, the method comprising a step of:administering the anti-TNF therapy to subjects who have been determinedto display a gene expression response signature established todistinguish between responsive and non-responsive prior subjects whohave received the anti-TNF therapy.

In some embodiments, the present disclosure provides methods furthercomprising determining, prior to the administering, that a subjectdisplays the gene expression response signature; and administering theanti-TNF therapy to the subject determined to display the geneexpression response signature.

In some embodiments, the present disclosure provides methods furthercomprising determining, prior to the administering, that a subject doesnot display the gene expression response signature; and administering atherapy alternative to anti-TNF therapy to the subject determined not todisplay the gene expression response signature.

In some embodiments, the gene expression response signature wasestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy by a method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness to a human interactomemap; and selecting a plurality of genes determined to cluster with oneanother in a human interactome map, thereby establishing the geneexpression response signature.

In some embodiments, the gene expression response signature wasestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy by a method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness to a human interactomemap; and selecting a plurality of genes determined to be proximal withone another in a human interactome map, thereby establishing the geneexpression response signature.

In some embodiments, the present disclosure provides methods furthercomprising steps of: validating the gene expression response signatureby assigning probability of response to a group of known responders andnon-responders; and checking the gene expression response signatureagainst a blinded group of responders and non-responders.

In some embodiments, the responsive and non-responsive prior subjectssuffered from the same disease, disorder, or condition.

In some embodiments, the subjects to whom the anti-TNF therapy isadministered are suffering from the same disease, disorder or conditionas the prior responsive and non-responsive prior subjects.

In some embodiments, the gene expression response signature includesexpression levels of a plurality of genes derived from a cluster ofgenes associated with response to anti-TNF therapy on a humaninteractome map.

In some embodiments, the gene expression response signature includesexpression levels of a plurality of genes proximal to genes associatedwith response to anti-TNF therapy on a human interactome map.

In some embodiments, the gene expression response signature includesexpression levels of a plurality of genes determined to cluster with oneanother in a human interactome map.

In some embodiments, the gene expression response signature includesexpression levels of a plurality of genes that are proximal in a humaninteractome map.

In some embodiments, genes of the subject are measured by at least oneof a microarray, RNA sequencing, real-time quantitative reversetranscription PCR (qRT-PCR), bead array, ELISA, and protein expression.

In some embodiments, the subject suffers from a disease, disorder, orcondition selected from rheumatoid arthritis, psoriatic arthritis,ankylosing spondylitis, Crohn's disease, ulcerative colitis, chronicpsoriasis, hidradenitis suppurativa, and juvenile idiopathic arthritis.

In some embodiments, the anti-TNF therapy is or comprises administrationof infliximab, adalimumab, etanercept, cirtolizumab pegol, goliluma, orbiosimilars thereof. In some embodiments, the anti-TNF therapy is orcomprises administration of infliximab or adalimumab.

In some embodiments, the present disclosure provides, in a method ofadministering anti-TNF therapy, the improvement that comprisesadministering the therapy selectively to subjects who have beendetermined to display a gene expression response signature establishedto distinguish between responsive and non-responsive prior subjects whohave received the anti-TNF therapy.

In some embodiments, the responsive and non-responsive prior subjectssuffered from the same disease, disorder, or condition.

In some embodiments, the subjects to whom the anti-TNF therapy isadministered are suffering from the same disease, disorder or conditionas the prior responsive and non-responsive prior subjects.

In some embodiments, the gene expression response signature includesexpression levels of a plurality of genes derived from a cluster ofgenes associated with response to anti-TNF therapy on a humaninteractome map.

In some embodiments, the anti-TNF therapy is or comprises administrationof infliximab, adalimumab, etanercept, cirtolizumab pegol, goliluma, orbiosimilars thereof.

In some embodiments, the disease, disorder, or condition is selectedfrom rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis,Crohn's disease, ulcerative colitis, chronic psoriasis, hidradenitissuppurativa, and juvenile idiopathic arthritis.

In some embodiments, the disease, disorder, or condition is rheumatoidarthritis.

In some embodiments, the disease, disorder, or condition is ulcerativecolitis.

In some embodiments, the present disclosure provides use of an anti-TNFtherapy in the treatment of a subject determined to display a geneexpression response signature established to distinguish betweenresponsive and non-responsive prior subjects who have received theanti-TNF therapy.

In some embodiments, prior to use of the anti-TNF therapy, determiningthat the subject displays the gene expression response signature. Insome embodiments, prior to use of the anti-TNF therapy, determining thatthe subject does not display the gene expression response signature.

In some embodiments, the gene expression response signature wasestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy by a method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness to a human interactomemap; and selecting a plurality of genes determined to cluster with oneanother in a human interactome map, thereby establishing the geneexpression response signature.

In some embodiments, the gene expression response signature wasestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy by a method comprisingsteps of: mapping genes whose expression levels significantly correlateto clinical responsiveness or non-responsiveness to a human interactomemap; and selecting a plurality of genes determined to be proximal withone another in a human interactome map, thereby establishing the geneexpression response signature.

In some embodiments, the gene expression response signature wasestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy by the method furthercomprising steps of validating the gene expression response signature byassigning probability of response to a group of known responders andnon-responders; and checking the gene expression response signatureagainst a blinded group of responders and non-responders.

Therapy Reimbursement

Provided technologies also provide significant value to regulatoryagencies and payers (i.e., insurance companies). As noted, in someembodiments, provided technologies provide consistent and reliable meansfor predicting anti-TNF therapy response for a given subject or cohortof subjects. As also noted, for some anti-TNF therapies, response rateof subjects is only about 65%. With a 35% chance that a given anti-TNFtherapy may have no or sub-optimal impact, or indeed a negative impacton a subject, it is clearly beneficial for regulatory agencies andpayers to be able to reliably predict when a subject will or will notrespond to a given therapy.

Generally, when a patient is prescribed a particular medication ortherapy, the patient obtains or receives the medication or therapy froma care provider, such as a pharmacy or a medical practitioner. Commonly,the pharmacy or medical practitioner pays for the medication outright,and then will either pass the cost on to the patient (if coverage froman insurance company/payer is not available), or will seek reimbursementfor the medication from the insurance company payer. If the medicationis covered, the payer reimburses the pharmacy or medical practitioner,and the patient either pays nothing, or a co-pay. This system operatesunder the assumption that the medication will be beneficial to thepatient. But, failure of a patient to respond to a given therapy resultsin considerable wasted expense (not to mention wasted time on the partof the patient), coupled with a chance that the patient's disease,disorder, or condition could progress, or that the patient may besubject to a side effect associated with a given anti-TNF therapy.Either scenario results in additional costs to the payer, as the payerhas already reimbursed the pharmacy or medical practitioner for thetherapy. These costs, however, could have been avoided in the firstinstance with proper analysis of the patient prior to administering thedrug. Accordingly, the present disclosure provides technologies forreimbursement of medical expenses relating to anti-TNF therapy.

Whether a payer will cover a given therapy is determined by a committeereferred to as the “Pharmacy and Therapeutics Committee” (the “P&TCommittee”). The P&T Committee is responsible for managing the“formulary” of the agency. The formulary is a continually updated listof medications and related information, and includes a list ofmedications and medication-associated products or devices, medicationuse policies, important ancillary drug information, decision supporttools, and organization guidelines for an agency or a payer. The P&TCommittee, continually reviewing and revising the formulary, establishespolicies regarding the use of drugs, therapies, and drug-relatedproducts, and identifies those that are most medically appropriate andcost-effective. Part of the review of the formulary is thepharmacoeconomic assessment, which includes cost-minimization to thepatient and payer, but also consideration of nonmedication-related costsand financial consequences of the pharmacy and the organization as awhole. Therefore, relevant to the instant disclosure, it is the job ofthe P&T Committee to determine whether certain diagnostic methods shouldbe required or associated with a particular medication. By requiringthat a subject obtain certain diagnostic information prior toadministration of a particular therapy, the payer can ensure that thesubject will indeed respond, and that no costs are wasted reimbursing asubject for therapy that did not work.

Accordingly, the P&T Committee can require that either priorauthorization or step therapy be required before dispensing specificmedications. Prior authorization requires the prescriber (i.e., thephysician) to receive pre-approval for prescribing a particularmedication in order for that medication to qualify for coverage. Drugsthat require prior authorization will not be approved for payment untilthe conditions for approval of the drug are met. Prior authorization isuseful because it can address the need to obtain additional clinicalpatient information. Examples of such information include clinicaldiagnosis, height, weight, laboratory results, previous medications, andnon-drug therapies related to a specific disease, disorder, orcondition. Within the instant disclosure, a payer can require aprescriber receive prior authorization for administering particularanti-TNF therapies. For example, the payer can require that the patientprove that they have the particular gene expression associated with thegene expression response signature described herein (i.e., is aresponder) prior to authorizing payment of a particular anti-TNFtherapy. The payer can also deny coverage of a particular anti-TNFtherapy if the patient is classified as a non-responder (i.e., does notexpress genes associated with the gene expression response signature).Requiring such prior authorization allows payers to avoid loss byreimbursing (covering) particular medications that may not betherapeutically effective for a given subject.

Further, similar to prior authorization, some payers may require thatpatients undergo step therapy prior to administering a particular drug.Step therapy is the requirement that a patient undergo a particular,approved, treatment prior to authorization of the requested drug. Forexample, for patients suffering from arthritis, a payer may require thata patient undergo therapy with non-steroidal anti-inflammatory drugs(NSAIDs) prior to administration of a different medication. Similarly,for example, a payer can require that a patient be classified as aresponder or a non-responder based on the gene expression responsesignatures described generally herein. For example, if a patient isclassified as a “non-responder” to a given anti-TNF therapy, the payercan require that the patient undergo alternative therapy prior tocovering the given anti-TNF therapy. Utilizing this strategy allows forthe payer to ensure that other venues of treatment are attempted beforetaking on the risk of paying for medication that is unlikely to have anyeffect for a given patient.

Accordingly, in some embodiments, the present disclosure providestechnologies for reimbursement of medical expenses related to anti-TNFtherapy. In some embodiments, the present disclosure providestechnologies for adjudicating and reimbursing a care provider foranti-TNF therapy, such methods comprising: receiving a transaction fromthe care provider requesting reimbursement for the anti-TNF therapy;aggregating data for the anti-TNF therapy from a formulary; determiningwhether satisfying prior authorization conditions is required for theanti-TNF therapy; determining an amount of reimbursement using dataaggregated from the formulary and whether the prior authorizationconditions are satisfied.

In some embodiments, the formulary requires the prior authorizationconditions are satisfied. In some embodiments, the prior authorizationconditions comprise determining whether a subject displays a geneexpression response signature established to distinguish betweenresponsive and non-responsive prior subjects who have received theanti-TNF therapy.

In some embodiments, the subject satisfies the prior authorizationconditions by expressing the gene expression response signature. In someembodiments, the care provider is reimbursed in an amount between 1% and100% of the cost of the anti-TNF therapy.

In some embodiments, the subject does not satisfy the priorauthorization conditions by not expressing the gene expression responsesignature. In some embodiments, the amount of reimbursement is 0%.

In some embodiments, the present disclosure provides a method ofreimbursing medical expenses associated with administration of anti-TNFtherapy to a subject, the method comprising: providing reimbursement ifthe subject had been determined to display a gene expression responsesignature established to distinguish between responsive andnon-responsive prior subjects who have received the anti-TNF therapy;and not providing reimbursement if the subject had not been determinedto display the gene expression response signature.

EXEMPLIFICATION

Examples below demonstrate gene expression response signatures(otherwise referred to as “classifiers” below) characteristic ofsubjects who do or do not respond to anti-TNF therapy.

Example 1: Determining Responder and Non-Responder PatientPopulations—Ulcerative Colitis

In accordance with the present disclosure, gene expression data fromsubjects diagnosed with ulcerative colitis (UC) who had receivedanti-TNF therapy was used to determine patients who are responders andnon-responders to anti-TNF therapy. This UC cohort (GSE12251) included23 patients diagnosed with UC, 11 of which did not respond toanti-TNF-therapy. The gene expression data for this cohort weregenerated using the Affymetrix™ platform.

The gene expression data was analyzed define a set of genes (responsesignature genes) whose expression patterns distinguish responders andnon-responders. To do this, genes with significant gene expressiondeviations between responders and non-responders were relied on. Unlikeconventional differential expression methods that look for high foldchanges in gene expression between two groups, the present disclosureprovides the insight that small but significant changes between twogroups of patients should be included. The present disclosure thusidentifies the source of a problem with conventional differentialexpression technologies.

Without wishing to be bound by any particular theory, the presentdisclosure provides an insight that small but significant differencesimpact responsiveness to therapy. Indeed, the present disclosure notesthat, given that patients in these cohorts are all diagnosed with thesame disease, they often may not manifest big FCs across genes. Thepresent disclosure demonstrates that even very small but significantchanges in gene expression will lead to a different treatment outcome.

Additionally, the present disclosure demonstrates that analysis of genesdisplaying small (but significant) expression differences, in context ofa human “interactome” map, defines signatures that reliably distinguishresponders from non-responders.

In-Cohort Analysis

Using a human interactome (“HI”) map of gene connectivity that revealsfeatures of underlying biology of response and is useful forunderstanding response signature genes.

The top 200 genes (as measured by p-value from lowest to highest) whoseexpression values across patients were significantly correlated toclinical outcome after treatment were selected and mapped to HI. It wasobserved that even though these genes have been found using the geneexpression data only, they form a significant cluster (module) on theHI, with the large connected component (“LCC,” i.e., classifier genes)being much bigger that what is expected by chance HI (FIGS. 1A and 1B).Existence of such significant modules (z-score>1.6) has been repeatedlyshown to be associated with underlying disease biology. See Barabási, etal., “Network medicine: a network-based approach to human disease,” Nat.Rev. Genet, 12(1):56-68 (January 2011); Hall et al, “Genetics and theplacebo effect: the placebome,” Trends Mol. Med., 21(5):285-294 (May2015); del Sol, et al., “Diseases as network perturbations,” Curr. Opin.Biotechnol., 21(4):566-571 (August 2010).

FIGS. 1A and 1B show the subnetwork containing the genes correlated tophenotypic outcome in UC cohort as well as their interactions. Asignificant number of genes found by gene expression analysis form theLCC of the subgraph. The LCC genes (classifier genes) were then utilizedto feed and train a probabilistic neural network. The result of theanalysis shows a near perfect classifier with an Area Under the Curve(AUC) of 0.98 and with 100% accuracy in predicting non-responders.

The performance of trained classifiers was validated using aleave-one-out cross validation approach. FIGS. 2A and 2B show thereceiver operator curves (ROC) as well as negative prediction power(predicting non-responders) of the classifier. The classifier is able todetect 70% of the non-responders within a cohort.

TABLE 2 No. Genes No. Genes Cohort ID Selected in HI LCC SizeSignificance GSE12251 200 193 41 2.33

Table 2 represents the number and topological properties (i.e., the sizeof the largest component on the network and its significance) ofresponse signature genes when mapped onto the network.

A known and major drawback of traditional gene expression analysis isthe inability to reproduce the results across different studies. SeeIoannidis J. P. A., “Why most published research findings are false,”PLoS Med. 2(8):e124 (2005); Goodman S. N., et al., “What does researchreproducibility mean?” Sci. Transl. Med., 8(341):341-353 (2016);Ioannidis J. P., et al. “Replication validity of genetic associationstudies.” Nat. Genet. 29:(3)306-309 (November 2001). Below, it is shownthat the methods and systems described herein are able to make highaccuracy predictions across cohorts. To estimate the power of theclassifier, the classifier is tested in a completely independent cohort(GSE14580) and in a blinded fashion. The independent UC cohort includes16 non-responders and 8 responders.

For cross-platform validation, the two cohorts were merged and batcheffects removed using the R package, ComBat, a tool used forbatch-adjusting gene expression data. See Johnson W. E., et al.,“Adjusting batch effects in microarray expression data using empiricalBayes methods,” Biostatistics 8(1), 118-127 (2007). The performance ofthe designed classifier was tested in the independent cohort(leave-one-batch-out cross validation). FIGS. 3A and 3B show the ROC andnegative prediction curves associated with cross-cohort performance ofthe designed classifier. The trained classifier shows significantly highperformance in the independent cohort with AUC of 0.78.

Aside from the high cross-cohort performance assessed by AUC,cross-cohort NPV (Negative Predictive Value) and TNR (True NegativeRate), which indicates the accuracy of detecting non-responders in ablind cohort, were also estimated (FIG. 3B). The cross-cohort validationshows that the classifier is able to predict at least 50% ofnon-responders (NPV=1, TNR=0.5). The classifier is able to detect morenon-responders (TNR>0.5), which results in slight drop in NPV (FIG. 3B).Nevertheless, regardless of the selected point on the curve, theclassifier meets or exceeds the commercial criteria (NPV of 0.9 and TNRof 0.5) set by health insurance companies.

Disease Biology of Non-Responders

The network defined by the analysis described herein provides insightsinto underlying biology of this response prediction. The classifiergenes within the response module were analyzed using GO terms toidentify the most highly enriched pathways. We found that inflammatorysignaling pathways (including TNF signaling) were highly enriched, aswere pathways linked to sumoylation, ubiquitination, proteasomefunction, proteolytic degradation and antigen presentation in immunecells. Thus, the network approach described herein has captured proteininteractions for selecting genes within the response module that clearlyreflect the biology of the disease and drug response at the independentpatient level and allow the accurate prediction of response to anti-TNFtherapies from a baseline sample.

Discussion

A known and significant problem with existing anti-TNF therapyapproaches is that “many patients do not respond to the . . . therapy(primary non-response—PNR), or lose response during the treatment(secondary loss of response—LOR).” See, e.g., Roda et al., ClinGastroentorl. 7:e135, January 2016. Specifically, reports indicate that“around 10-30% of patients do not respond to the initial treatment and23-46% of patients lose response over time” Id. Thus, overall, the drugresponse rate for anti-TNF therapy (and in particular for anti-TNFtherapy to treat UC patients) is below 65%, resulting in continueddisease progression and escalating treatment costs for the majority ofthe patient population. Moreover, billions of dollars are spentprescribing anti-TNF therapies to patients that don't respond. There isa significant need for development of a technology that can identifyresponder vs non-responder subjects, prior to initiation of therapy, atthe time that therapy (e.g., a particular dose) is administered, and/orover time as therapy has been or is received.

Gene expression data has been touted as holding the promise of beingable to uncover disease biology of individual patients in complexdiseases, but up until now the data has been difficult to interpret, andefforts to develop biomarkers (e.g., expression signatures) fortherapeutic responsiveness have failed in cross-cohort validation tests.The present disclosure provides new technologies that, for example,consider relatively small changes in expression levels and/orparticipation of genes in relevant parts of the human interactome.

As already noted, the present disclosure demonstrates that projectingbaseline gene expression profiles from UC patients that arenon-responders to anti-TNF therapy on the HI, reveals that such profilescluster and form a large connected module that describes thenon-responders' disease biology. In accordance with the presentinvention, a classifier developed from genes expressed in this modulepredicts non-response with a high level of accuracy and has beenvalidated in a completely independent cohort (cross-cohort validation).Furthermore, this classifier meets the commercial criteria set byinsurance companies and is therefore ready for clinical development andfuture commercialization.

Methods

Microarray Analysis

Cohort 1, GSE14580: Twenty-four patients with active UC, refractory tocorticosteroids and/or immunosuppression, underwent colonoscopy withbiopsies from diseased colon within a week prior to the firstintravenous infusion of 5 mg infliximab per kg body weight. Response toinfliximab was defined as endoscopic and histologic healing at 4-6 weeksafter first infliximab treatment using the MAYO score. Six controlpatients with normal colonoscopy were included. Total RNA was isolatedfrom colonic mucosal biopsies, labelled and hybridized to Affymetrix™Human Genome U133 Plus 2.0 Arrays.

Cohort 2, GSE12251: Twenty-two patients underwent colonoscopy withbiopsy before infliximab treatment. Response to infliximab was definedas endoscopic and histologic healing at week 8 using the MAYO score (P2,5, 9, 10, 14, 15, 16, 17, 24, 27, 36, and 45 as responders; P3, 12, 13,19, 28, 29, 32, 33, 34, and 47 as non-responders). Messenger RNA wasisolated from pre-infliximab biopsies, labeled and hybridized toAffymetrix™ HGU133 Plus_2.0 Array.

Identification of Classifier Genes

Genes with expression values across patients that were significantlycorrelated to clinical measures after treatment were selected as bestdeterminants of response. These genes were mapped on the consolidatedHuman Interactome (“HI”). The consolidated Human Interactome collectsphysical protein interactions between a cell's molecular componentsrelying on experimental support. The material reported by Barabási etal. in “Uncovering disease-disease relationships through the incompleteinteractome,” Science, 347(6224):1257601 (February 2015), the entiretyof which is incorporated herein by reference, provides instructionregarding how to build and curate a Human Interactome. The genes on theHuman Interactome are not randomly scattered on the network. Instead,they significantly interact with each other, reflecting the existence ofan underlying disease biology module that explains response.

Human Interactome

As noted, the HI contains experimentally supported physical interactionsbetween cellular components. These interactions were queried fromseveral resources but only selected those that are supported byexperimental validation. Most of the interactions in the HI are fromunbiased high-throughput studies such as Y2H. All included data wereexperimentally supported interactions that have been reported in atleast two publications. These interactions include, regulatory,metabolic, signaling and binary interactions. The HI contains about 17kcellular components and over 200K interactions among them. Unlike otherinteraction databases, no computationally inferred interaction wereincluded, nor any interaction curated from text parsing of literaturewith no experimental validation.

Classifier Design and Validation

Genes identified above were used as features of a probabilistic neuralnetwork. The classifier was validated using leave-one-out and/or k-foldcross validation within a given cohort. The classifier was trained basedon the outcome data provided on all patients but the one left out. Theclassifier was blind to the response outcome of that left out patient.Predicting the outcome of the patient that has been left out thenvalidated the trained classifier. This procedure was repeated so thateach patient was left out once. The classifier provided a probabilityfor each patient reflecting whether they belong to responder ornon-responder group. The logarithm of likelihood ratio was used toassign a score to each patient. Patients were then ranked based on theirscore and prediction accuracy values were estimated by varying theclassifier threshold resulting in the ROC curves. In particular, eachpatient is given a score by the trained classifier. The predictionaccuracy is measured for the entire cohort as a whole and by checkingwhether given scores across patients well distinguish responders andnon-responders. Prediction performance is generally measured by the AreaUnder the Curve (AUC). When higher levels of accuracy are required,negative predictive value (NPV) and true negative rate (TNR) can beused. The score cutoff that results in best group separation (e.g.,highest NPV) is set for future predictions.

Example 2: Determining Responder and Non-Responder PatientPopulations—Rheumatoid Arthritis

Analogous to Example 1, the present Example 2 describes prediction ofresponse and/or non-response to anti-TNF therapy in patients sufferingfrom rheumatoid arthritis (RA). The presently described predictionssatisfy the performance threshold identified by payers and physicians ofNegative Predictive Value (NPV) of 0.9 and True Negative Rate (TNR) of0.5.

In the present example, gene expression data from baseline blood samplesfor two cohorts comprising a total of 89 RA patients were analyzed. Themethodology utilized in the present Example to develop a classifier(i.e., a gene expression response signature) that predicted responseand/or non-response to anti-TNF therapy included a four step process.First, initial genes were selected based on differential expressionbetween responders and non-responders to anti-TNF therapy. Second, suchgenes were projected on the human interactome to determine which genesform a significant and biologically relevant cluster. Third, genes thatcluster on the interactome were selected and fed into a probabilisticneural network (PNN) to develop the final classifiers. And fourth, eachclassifier was validated using leave-one-out validation in the trainingset, and validated cross-cohort in an independent cohort of patients(test set). For RA, the final classifier contained 9 genes and reachedan NPV of 0.91 and TNR of 0.67 in the test set.

The developed classifiers meet the performance thresholds set by payersand physicians; those skilled in the art will appreciate that theseclassifiers are useful tests that predict non-response to anti-TNFsprior to initiation of therapy and/or to assess desirability of alteringadministered therapy. Among other things, provided technology thereforepermits selection of therapy (whether initial therapy or continued oraltered therapy), including enabling patients to be switched ontoalternative therapies faster, resulting in substantial clinical benefitsto patients and savings to the healthcare system.

Data Description

The response prediction analysis in RA utilized in the present Examplewas based on two individual cohorts (Tables 3 and 4). Response wasmeasured 14-weeks after initiation of anti-TNF therapy, with responserates (Good responders; DAS28 improvement>1.2, corresponding to LDA orremission) in cohort 1 and 2 of 30% and 23%, respectively. Cohort 1 wasused to train the classifier and cohort 2 was used as the independenttest cohort to validate the predictive power of the classifier.

The analyses were conducted on RNA expression data generated from wholeblood, before initiation of therapy, using an Illumina™ BeadArray™platform and provided as standard output of BeadStudio™. Raw data wasnormalized and processed using lumi package in R.

TABLE 3 Clinical response according to EULAR DAS28 criteria Cohort 1Cohort 2 No. Good responders 15 9 No. Moderate responders 15 15 No.Non-responders 20 15

TABLE 4 DAS28 Improvement Baseline DAS28 >1.2 >0.6 & ≤1.2 ≤0.6 ≤3.2 GoodModerate No response response response >3.2 & ≤5.1 Moderate Moderate Noresponse response response  >5.1 Moderate No response No responseresponse

Identifying Classifier Genes

Expression values for over 10,000 probes (genes) were available in eachpatient; those skilled in the art will appreciate the challengesassociated with defining a set of genes (features) that effectivelydistinguishes response from such a volume of data. Insights provided bythe present disclosure, including that particularly useful genes forinclusion in a classifier may, in some embodiments, be those withrelatively small changes, permit effective selection of gene (feature)set(s) for use in a classifier.

In the present Example, genes for inclusion in an RA classifier wereselected via a multi-step analysis: First, genes were ranked based ontheir significance of correlation to patient's response outcome (changein baseline DAS28 score at week 14) using Pearson correlation resultingin 200 top ranked genes (Feature set 1). Unlike conventionaldifferential expression methods that look for highest fold changes ingene expression between two groups, the present Example captures smallbut significant changes between two groups of patients.

Second, the present disclosure appreciates that gene products (proteins)do not function in isolation, and furthermore appreciates that referenceto the interactome—a map of protein interconnectivity—can valuably beused as a blueprint to understand roles played by individual geneproducts in context (i.e., in biology of cells and/or organisms). Bymapping the 200 genes identified above on the interactome, a significantcluster, or response module, consisting of 41 proteins was identified(Table 5). Existence of a significant cluster was repeatedly shown to beassociated with underlying disease biology. The observed response modulenot only uncovers the underlying biology of response but also served asFeatures set 2. In particular, FIG. 6 illustrates a classifierdevelopment flowchart containing identifying features of the classifier(A), training and validation of a probabilistic neural network on cohort1 using identified features (B) and validation of the trained classifierusing identified feature genes expressions in an independent cohort (C).The final set of features are selected based on best performance.

TABLE 5 #Top genes #Genes Cohort ID selected in HI LCC size Significance1 200 186 41 1.19

Training the Response Classifier and In-Cohort Validation

In the present Example, a response classifier was trained by feeding aprobabilistic neural network with Feature set 1 and 2. Training theclassifier on Feature set 1 significantly predicted response usingleave-one-out cross validation and reached an AUC of 0.69, an NPV of 0.9and a TNR of 0.52 (FIG. 4A, and FIG. 4B, respectively), outperformingFeature set 2. Having a smaller number of classifier genes also opens upthe opportunity to use a variety of lower cost, FDA-approved expressionplatforms with a broad installed base to generate the required geneexpression data sets. The classifier was therefore further trained tosee if performance holds up when reducing the number of genes in Featureset 1 by training on top n-ranked genes where n goes from 1 to 20. Alocal maximum was observed in classifier performance when training onthe top 9 genes (AUC=0.74, corrected p-value=0.006) with an NPV of 0.92and a TNR of 0.76 (FIG. 4C and FIG. 4D). The 9-gene classifier waschosen for the cross cohort validation analysis below.

Validation of Trained Response Classifier in an Independent Cohort(Cross-Cohort Validation)

Of critical importance when building diagnostic tests and classifiers isthe ability to reproduce the results and successfully test theclassifier's performance in an independent cohort. The developed 9-geneclassifier was therefore tested in a blinded fashion on a completelyindependent group of patients (cohort 2). The results show that theclassifier performed well (cross-cohort AUC=0.78, p value=0.01) with anNPV of 0.91 and a TNR of 0.67 (FIG. 5B and Table 6). FIG. 5A is an ROCcurve of cross-cohort classifier test results.

TABLE 6 Predicting non-responders for TNF-naïve patients AUC NPV TNRClassifier trained on cohort 1 tested on cohort 2 0.78 0.91 0.67

Discussion

The present Example documents effectiveness of a classifier, asdescribed herein, that predicts non-response to anti-TNF drugs beforetherapy is prescribed in patients suffering from RA.

Interviews with payers and clinicians indicate that current targetspecifications aim to identify at least half of the non-responders toanti-TNF therapy with high negative predictive accuracy (NPV>90%).Patients that are identified as non-responders can be placed onalternative effective therapies and higher response rates for thosepatients still offered anti-TNFs can be achieved. Financial savings aregarnered by not spending on expensive ineffectual therapies and avoidingserious side effects and continuing disease progression. By identifying50% of the non-responders, significant cost and care benefits can beachieved since, in the absence of stratification, two-thirds of patientsdo not achieve the target of LDA or remission today. High NPV is desiredto ensure that few patients that would have responded are notincorrectly withheld a therapy they would have benefited from.

For RA, the present disclosure has demonstrated an AUC of 0.78, an NPVof 0.91 and a TNR of 0.67, resulting in the matrix below (Table 7). Thatis, the classifier identifies 67% of true non-responders with a 91%accuracy. Stratifying patients using this classifier would increase theresponse rate for the anti-TNF treated group by 71% from 34% to 58%. Bycomparison, the highest cross-cohort performance reported forclassifiers developed by others had an NPV of 0.71 and a TNR of 0.71.See Toonen E J. et al. “Validation study of existing gene expressionsignatures for anti-TNF treatment in patients with rheumatoidarthritis.” PLoS One. 2012; 7(3):e33199. Using that classifier wouldsignificantly misclassify the genuine responders leading to a worseoverall response rate than not using it at all. The presently describedclassifiers clearly meet the performance targets when tested in anindependent cohort of patients.

TABLE 7 Predicted R NR Actual R 30 4 34 TPR 87% NR 22 44 66 TNR 67% 5248 PPV 58% NPV 91%

The reduced number of genes in the classifier allows several expressionanalysis platforms to be considered for the delivery of the finalcommercial version of the test. For example, Nanostring nCounter systemuses digital barcode technology to count nucleic acid analytes forpanels of up to several hundred genes on an FDA approved platform.Multiplexed qRT-PCR is the gold standard for quantifying gene expressionfor panels of less than ˜20 genes and would enable the test to beoffered as a distributable kit. RA is a chronic, complex autoimmunediseases, where many genetic risk factors have been identified but noneof them are of sufficient impact to be useful as diagnostic orprognostic markers. The present disclosure provides a ranked list ofcandidate genes based on correlation of baseline expression level withresponse outcomes. The rank order is derived from the significance ofthe correlation. The present disclosure, however, does not prioritizegenes with larger fold change across the category of responders andnon-responders. It is common practice in the field to give preference togenes that show the highest fold change. This is because it is generallybelieved that large changes in expression levels are biologically moremeaningful, and because of the technical advantage of high signal tonoise ratios to compensate for high background and other sources oftechnical variability. However, the present disclosure appreciates thatsmall differences, which are ignored or overlooked in many conventionaltechnologies, can provide important, and even critical, discriminatingcapability. Without wishing to be bound by any particular theory, thepresent disclosure proposes that subtle differential perturbations maybe particularly relevant and/or important in situations, like thepresent, where subjects suffering from the same disease, disorder, orcondition are compared with one another (e.g., rather than with“control” subjects not suffering from the disease, disorder, orcondition). It may be that small yet statistically significantdifferences in gene expression differentiate patient populations incomplex diseases such as RA. This study shows that even very small butsignificant changes in gene expression will lead to a differenttreatment outcome. This method captures genes that are overlooked byconventional differential expression analysis.

Additionally, the present disclosure utilizes the highly unbiased andindependently validated map of the protein-protein interactions incells, the human interactome. By mapping the prioritized genes to theinteractome, distinct and statistically significant clusters appear. Inaddition to using the interactome network analysis to define theclassifier, the identified clusters also provide biological insightsinto the biology and causal genes of anti-TNF response. The genescorresponding to the top 9 genes in RA are valuable in immunologicalpathways and functions linked to ER stress, the protein quality controlpathway, control of the cell cycle and the ubiquitin proteasome system,primarily in targeting key regulators of the cell cycle to theproteasome through ubiquitinyation.

The classifiers described here serve as the basis for diagnostic teststo predict anti-TNF non-response for patients with moderate to severedisease and considering initiating biologic therapy. Patients identifiedas non-responders will be offered alternative, approved mechanism ofaction therapies. These tests will provide significant improvements tocurrent clinical practice by increasing the proportion of patientsreaching treatment goals, making the treatment assignment based onscientific data and as a result decrease waste of resources and generatesignificant financial savings within the health care system.

Materials and Methods

RA Cohort Description and Microarray Analysis

Blood samples were collected from RA patients across the United Statesfrom two individual observational studies, both of which predominantlyconsisted of older Caucasian women. Cohort 1 was obtained from amulti-center study conducted in 2014. These patients were treated withEnbrel, Remicade, Humira, Cimzia and Simponi. Cohort 2 was obtained fromthe Autoimmune Biomarkers Collaborative Network, a NIAMS supportedcontract to develop new approaches to biomarkers for RA and lupus in2003. These patients were treated with Humira, Remicade and Enbrel.

The level of response was defined using the EULAR DAS28 scoring criteriaassessed 14 weeks after anti-TNF treatment. EULAR response rates forfemale TNF naïve patients are given in Table 1. EULAR responsecharacterizes patients into good responders, moderate responders andnon-responders. For this study, response was defined as EULAR goodresponse, or DAS28 improvement>1.2. This corresponds to LDA orremission.

The gene expression data and 14 week response outcome was available for50 and 39 female and TNF naïve samples in cohort 1 and 2, respectively,for classifier design and validation.

All subjects had PaxGene® tubes drawn at baseline before startingtherapy, and again at 14 weeks after treatment started. RNA was isolatedusing the QIAcube® (Qiagen®) following the manufacturer's automatedprotocol for PaxGene® blood RNA. Extracted samples were eluted in 80 ulof elution buffer (BR5) and subsequently run on Agilent's 2100Bioanalyzer™ of RNA integrity using the RNA 6000 Nanochip. Samples withRNA Integrity Numbers (RIN)>6.5 were diluted to 30 ng/μl in a total 11μl of RNAse-free water. Samples were amplified using Life TechnologiesIllumina™ RNA Total Prep™ Amplification Kit. 750 ng of cRNA wasre-suspended in 5 μl of RNAse-free water for analysis on the Illumina™Human HT-1.2v4 chip (cohort 1 samples) and 1.2 μg was re-suspended in 10μl of RNAse-free water for analysis Illumina™ WG6v3 Bead Chip (cohort 2samples). All samples were processed according to the manufacturer'sinstructions.

Raw data were exported from GenomeStudio™ and further analyzed with theR programming language. All datasets were background corrected using theR/Bioconductor package “lumi.” Data were further transformed usingvariance stabilization transformational (vst) and quantile normalized.Probes with zero detection count and detection rates of lower that 50%across samples were removed from the study. To enable cross cohortclassifier testing, the two cohorts were combined and normalized usingthe ComBat package in R and then separated to ensure completely blindtesting. All of the microarray analysis resulted in having about 10,000common probes in the two cohorts.

Identification of Classifier Genes

Genes with expression values that are significantly correlated toclinical measures after treatment are selected as the best determinantsof response. Expression correlation of gene expression to responseoutcome is measured by Pearson correlation. Genes are ranked based onthe correlation value and the performance of the classifier is assessedwhen using top n ranked genes. In some cases mapping the ranked genes onthe interactome forms a significant cluster reflecting the underlyingbiology of response. It is observed that the ranked genes are notrandomly scattered on the network. Instead, they significantly interactwith each other, reflecting the existence of an underlying diseasebiology module that explains response.

Classifier Design and Validation

Genes identified in the previous step were used as features of aprobabilistic neural network. In this approach the average distance ofeach sample to training samples' probability distribution functions iscalculated. The average distance of a test sample to training samples inthe n-dimensional feature space determines the probability of belongingto one group vs. the other. The classifier was validated usingleave-one-out cross validation within a given cohort. In this approach,the classifier was trained based on the outcome data provided on allpatients but the one left out. The classifier was blind to the responseoutcome of that left out patient. Predicting the outcome of the patientthat has been left out then validated the trained classifier. Thisprocedure was repeated so that each patient was left out once. Theclassifier provided a probability for each patient reflecting whetherthey responded or not. These probabilities were used to define a score(by using log of likelihood ratio) for each patient. The area under thecurve (AUC) determined the performance of the classifier. Incross-cohort assessment of the classifier, the trained classifier wascompletely blind to the outcome of the independent cohort. Trained dataon one cohort is tested to determine its ability to predict response inan independent cohort.

Statistical Analysis

Fisher's t-test was used to determine the significance of differencebetween two distributions.

Human Interactome

The human interactome contains experimentally supported physicalinteractions between cellular components. These interactions arecollected from several resources but only those supported by a rigorousexperimental validation confirming the existence of a physicalinteraction between proteins are selected. Most of the interactions inthe interactome are from unbiased high-throughput studies such as yeast2-hybrid. Experimentally supported interactions that that have beenreported in at least two publications are also included. Theseinteractions include regulatory, metabolic, signaling and binaryinteractions. The interactome contains about 17,000 cellular componentsand over 200,000 interactions. Unlike other interaction databases thepresent methods do not include any computationally inferredinteractions, nor any interaction curated from text parsing ofliterature with no experimental validation. Therefore, the interactomeused is the most complete, carefully selected and quality controlledversion to date.

The foregoing has been a description of certain non-limiting embodimentsof the subject matter described within. Accordingly, it is to beunderstood that the embodiments described in this specification aremerely illustrative of the subject matter reported within. Reference todetails of the illustrated embodiments is not intended to limit thescope of the claims, which themselves recite those features regarded asessential.

It is contemplated that systems and methods of the claimed subjectmatter encompass variations and adaptations developed using informationfrom the embodiments described within. Adaptation, modification, orboth, of the systems and methods described within may be performed bythose of ordinary skill in the relevant art.

Throughout the description, where systems are described as having,including, or comprising specific components, or where methods aredescribed as having, including, or comprising specific steps, it iscontemplated that, additionally, there are systems encompassed by thepresent subject matter that consist essentially of, or consist of, therecited components, and that there are methods encompassed by thepresent subject matter that consist essentially of, or consist of, therecited processing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as any embodiment of the subjectmatter described within remains operable. Moreover, two or more steps oractions may be conducted simultaneously.

1. A method of treating subjects with anti-TNF therapy, the methodcomprising a step of: administering the anti-TNF therapy to subjects whohave been determined to display a gene expression response signatureestablished to distinguish between responsive and non-responsive priorsubjects who have received the anti-TNF therapy.
 2. The method of claim1, wherein the responsive and non-responsive prior subjects sufferedfrom the same disease, disorder, or condition.
 3. The method of claim 2,wherein the subjects to whom the anti-TNF therapy is administered aresuffering from the same disease, disorder or condition as the priorresponsive and non-responsive prior subjects.
 4. The method of claim 1,wherein the gene expression response signature includes expressionlevels of a plurality of genes derived from a cluster of genesassociated with response to anti-TNF therapy on a human interactome map.5. The method of claim 1, wherein the gene expression response signatureincludes expression levels of a plurality of genes associated withresponse to anti-TNF therapy and characterized by their topologicalproperties when mapped on a human interactome map.
 6. The method ofclaim 1, wherein the anti-TNF therapy is or comprises administration ofinfliximab, adalimumab, etanercept, cirtolizumab pegol, goliluma, orbiosimilars thereof.
 7. The method of claim 6, wherein the anti-TNFtherapy is or comprises administration of infliximab or adalimumab. 8.The method of claim 1, wherein the disease, disorder, or condition isselected from rheumatoid arthritis, psoriatic arthritis, ankylosingspondylitis, Crohn's disease, ulcerative colitis, chronic psoriasis,hidradenitis suppurativa, and juvenile idiopathic arthritis.
 9. Themethod of claim 1, further comprising: determining, prior to theadministering, that a subject displays the gene expression responsesignature; and administering the anti-TNF therapy to the subjectdetermined to display the gene expression response signature.
 10. Themethod of claim 1, further comprising: determining, prior to theadministering, that a subject does not display the gene expressionresponse signature; and administering a therapy alternative to anti-TNFtherapy to the subject determined not to display the gene expressionresponse signature.
 11. The method of claim 10, wherein the therapyalternative to anti-TNF therapy is selected from rituximab, sarilumab,tofacitinib citrate, lefunomide, vedolizumab, tocilizumab, anakinra, andabatacept.
 12. The method of claim 9, wherein the step of determiningcomprises measuring gene expression by at least one of a microarray, RNAsequencing, real-time quantitative reverse transcription PCR (qRT-PCR),bead array, and ELISA.
 13. The method of claim 1, wherein the geneexpression response signature was established to distinguish betweenresponsive and non-responsive prior subjects who have received theanti-TNF therapy by a method comprising steps of: mapping genes whoseexpression levels significantly correlate to clinical responsiveness ornon-responsiveness to a human interactome map; and selecting a pluralityof genes determined to cluster with one another in a human interactomemap, thereby establishing the gene expression response signature. 14.The method of claim 1, wherein the gene expression response signaturewas established to distinguish between responsive and non-responsiveprior subjects who have received the anti-TNF therapy by a methodcomprising steps of: mapping genes whose expression levels significantlycorrelate to clinical responsiveness or non-responsiveness to a humaninteractome map; and selecting a plurality of genes determined todemonstrate similar topological patterns in a human interactome map,thereby establishing the gene expression response signature.
 15. Themethod of claim 13, further comprising steps of: validating the geneexpression response signature by assigning probability of response to agroup of known responders and non-responders; and checking the geneexpression response signature against a blinded group of responders andnon-responders.
 16. A kit comprising a gene expression responsesignature established to distinguish between responsive andnon-responsive prior subjects who have received anti-TNF therapy.
 17. Amethod of determining a gene expression response signature, the methodcomprising steps of: mapping genes whose expression levels significantlycorrelate to clinical responsiveness or non-responsiveness of anti-TNFtherapy to a human interactome map; and selecting a plurality of genesdetermined to cluster exhibit certain network topological propertieswith one another in a human interactome map, thereby establishing thegene expression response signature.